cleaning and refactor
This commit is contained in:
Mitchell Wortsman
parent
30d21d585c
commit
7f87ba83ee
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@ -3,4 +3,4 @@
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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from .modules import Int8Params, Linear8bitLt, StableEmbedding, OutlierAwareLinear, Fake4bitLinear, LinearFP8, LinearInt8, Linear8bitLtThresh, LinearInt8Cast, Linear8bitLt2, Linear8bitLtMixed, LinearFP8Global, LinearFP4, LinearFP8Mixed
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from .triton_based_modules import SwitchBackLinear, SwitchBackGlobalLinear
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from .triton_based_modules import SwitchBackLinear, SwitchBackLinearGlobal, SwitchBackLinearVectorized, StandardLinear
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@ -1,26 +1,76 @@
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import torch
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import torch.nn as nn
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import time
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from functools import partial
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from .triton_utils.v0.quantize_rowwise_nogroup import quantize_rowwise_nogroup
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from .triton_utils.v0.quantize_columnwise_nogroup_transpose import quantize_columnwise_nogroup_transpose
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from .triton_utils.v0.int8_matmul_rowwise_dequantize_bias import int8_matmul_rowwise_dequantize_bias
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from .triton_utils.v0.quantize_rowwise import quantize_rowwise
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from .triton_utils.v0.quantize_columnwise_and_transpose import quantize_columnwise_and_transpose
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from .triton_utils.v0.int8_matmul_rowwise_dequantize import int8_matmul_rowwise_dequantize
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from .triton_utils.v0.quantize_global import quantize_global, quantize_global_transpose
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from .triton_utils.v0.int8_matmul_mixed_dequanitze import int8_matmul_mixed_dequanitze, int8_matmul_mixed_dequanitze_bias
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from .triton_utils.v0.fused_gelu_quantize import quantize_rowwise_nogroup_gelu, quantize_rowwise_nogroup_back_gelu
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from .triton_utils.v0.int8_matmul_mixed_dequanitze import int8_matmul_mixed_dequanitze
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class _switchback(torch.autograd.Function):
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class _switchback_global(torch.autograd.Function):
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@staticmethod
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def forward(ctx, X_3D, W, bias):
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# reshape input to [N * L, D]
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X = X_3D.view(-1, X_3D.size(-1))
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# rowwise quantize for X, global quantize for W
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X_int8, state_X = quantize_rowwise(X)
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W_int8, state_W = quantize_global(W)
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# save for backward.
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ctx.save_for_backward = X, W
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# matmult, fused dequant and add bias
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# call "mixed" because we are mixing rowwise quantized and global quantized
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return int8_matmul_mixed_dequanitze(
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X_int8, W_int8.t(), state_X, state_W, bias
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).view(*X_3D.size()[:-1], -1)
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@staticmethod
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def backward(ctx, G_3D):
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# reshape input to [N_out * L, D]
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G = G_3D.reshape(-1, G_3D.size(-1))
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grad_X = grad_W = grad_bias = None
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X, W = ctx.save_for_backward
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if ctx.needs_input_grad[0]:
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# rowwise quantize for G, global quantize for W
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# for W, we also fuse the transpose operation because only A @ B^T is supported
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# so we transpose once then call .t() in the matmul
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G_int8, state_G = quantize_rowwise(G)
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W_int8, state_W = quantize_global_transpose(W)
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grad_X = int8_matmul_mixed_dequanitze(G_int8, W_int8.t(), state_G, state_W, None).view(
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*G_3D.size()[:-1], -1
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)
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if ctx.needs_input_grad[1]:
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# backward pass uses standard weight grad
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grad_W = torch.matmul(G.t(), X.to(G.dtype))
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if ctx.needs_input_grad[2]:
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grad_bias = G.sum(dim=0)
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return grad_X, grad_W, grad_bias
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class _switchback_vectorrize(torch.autograd.Function):
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@staticmethod
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def forward(ctx, X_3D, W, bias):
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# reshape input to [N * L, D]
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X = X_3D.view(-1, X_3D.size(-1))
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ctx.save_for_backward = X, W
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X_int8, state_X = quantize_rowwise_nogroup(X)
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W_int8, state_W = quantize_rowwise_nogroup(W)
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return int8_matmul_rowwise_dequantize_bias(
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# rowwise quantize for X
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# columnwise quantize for W (first rowwise, transpose later)
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X_int8, state_X = quantize_rowwise(X)
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W_int8, state_W = quantize_rowwise(W)
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# matmult, fused dequant and add bias
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# call kernel which expects rowwise quantized X and W
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return int8_matmul_rowwise_dequantize(
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X_int8, W_int8.t(), state_X, state_W, bias
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).view(*X_3D.size()[:-1], -1)
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@ -33,12 +83,15 @@ class _switchback(torch.autograd.Function):
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grad_X = grad_W = grad_bias = None
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if ctx.needs_input_grad[0]:
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G_int8, state_G = quantize_rowwise_nogroup(G)
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W_int8, state_W = quantize_columnwise_nogroup_transpose(W)
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grad_X = int8_matmul_rowwise_dequantize(G_int8, W_int8.t(), state_G, state_W).view(
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# rowwise quantize for G, columnwise quantize for W and fused transpose
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# we call .t() for weight later because only A @ B^T is supported
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G_int8, state_G = quantize_rowwise(G)
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W_int8, state_W = quantize_columnwise_and_transpose(W)
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grad_X = int8_matmul_rowwise_dequantize(G_int8, W_int8.t(), state_G, state_W, None).view(
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*G_3D.size()[:-1], -1
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)
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if ctx.needs_input_grad[1]:
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# backward pass uses standard weight grad
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grad_W = torch.matmul(G.t(), X.to(G.dtype))
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if ctx.needs_input_grad[2]:
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grad_bias = G.sum(dim=0)
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@ -46,11 +99,37 @@ class _switchback(torch.autograd.Function):
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return grad_X, grad_W, grad_bias
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class SwitchBackLinear(nn.Linear):
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def __init__(
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self,
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in_features: int,
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out_features: int,
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bias: bool = True,
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device=None,
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dtype=None,
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vectorize: bool = False
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):
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super().__init__(in_features, out_features, bias, device, dtype)
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# By default, we use the global quantization.
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self.vectorize = vectorize
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if self.vectorize:
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self._fn = _switchback_vectorrize
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else:
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self._fn = _switchback_global
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def prepare_for_eval(self):
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state_W = self.weight.abs().max(dim=1, keepdim=True)[0]
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W_int8 = (127 * self.weight.float() / state_W).round().to(torch.int8)
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state_W = state_W.squeeze()
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# If we just want to do eval, we can pre-quantize the weights instead of doing it on the forward pass.
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# Note this is experimental and not tested thoroughly.
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# Note this needs to be explicitly called with something like
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# def cond_prepare(m):
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# if hasattr(m, "prepare_for_eval"):
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# m.prepare_for_eval()
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# model.apply(cond_prepare)
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print('=> preparing for eval.')
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if self.vectorize:
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W_int8, state_W = quantize_rowwise(self.weight)
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else:
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W_int8, state_W = quantize_global(self.weight)
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self.register_buffer("W_int8", W_int8)
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self.register_buffer("state_W", state_W)
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@ -59,80 +138,29 @@ class SwitchBackLinear(nn.Linear):
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def forward(self, x):
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if self.training:
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return _switchback.apply(x, self.weight, self.bias)
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return self._fn.apply(x, self.weight, self.bias)
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else:
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if not hasattr(self, "state_W"):
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self.prepare_for_eval()
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# If it hasn't been "prepared for eval", run the standard forward pass.
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if not hasattr(self, "W_int8"):
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return self._fn.apply(x, self.weight, self.bias)
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# Otherwise, use pre-computed weights.
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X = x.view(-1, x.size(-1))
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X_int8, state_X = quantize_rowwise_nogroup(X)
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return int8_matmul_rowwise_dequantize_bias(
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X_int8, self.W_int8.t(), state_X, self.state_W, self.bias
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).view(*x.size()[:-1], -1)
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class _switchback_global(torch.autograd.Function):
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@staticmethod
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def forward(ctx, X_3D, W, bias):
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X = X_3D.view(-1, X_3D.size(-1))
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X_int8, state_X = quantize_rowwise_nogroup(X)
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W_int8, state_W = quantize_global(W)
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ctx.save_for_backward = X, W
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return int8_matmul_mixed_dequanitze_bias(
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X_int8, W_int8.t(), state_X, state_W, bias
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).view(*X_3D.size()[:-1], -1)
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@staticmethod
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def backward(ctx, G_3D):
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G = G_3D.reshape(-1, G_3D.size(-1))
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grad_X = grad_W = grad_bias = None
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X, W = ctx.save_for_backward
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if ctx.needs_input_grad[0]:
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G_int8, state_G = quantize_rowwise_nogroup(G)
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W_int8, state_W = quantize_global_transpose(W)
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grad_X = int8_matmul_mixed_dequanitze(G_int8, W_int8.t(), state_G, state_W).view(
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*G_3D.size()[:-1], -1
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)
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if ctx.needs_input_grad[1]:
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grad_W = torch.matmul(G.t(), X.to(G.dtype))
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if ctx.needs_input_grad[2]:
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grad_bias = G.sum(dim=0)
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return grad_X, grad_W, grad_bias
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class SwitchBackGlobalLinear(nn.Linear):
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def prepare_for_eval(self):
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state_W = self.weight.abs().max()
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W_int8 = (127 * self.weight.float() / state_W).round().to(torch.int8)
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self.register_buffer("W_int8", W_int8)
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self.register_buffer("state_W", state_W)
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del self.weight
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def forward(self, x):
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if self.training:
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return _switchback_global.apply(x, self.weight, self.bias)
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else:
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if not hasattr(self, "state_W"):
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self.prepare_for_eval()
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X = x.view(-1, x.size(-1))
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X_int8, state_X = quantize_rowwise_nogroup(X)
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return int8_matmul_mixed_dequanitze_bias(
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X_int8, self.W_int8.t(), state_X, self.state_W, self.bias
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).view(*x.size()[:-1], -1)
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X_int8, state_X = quantize_rowwise(X)
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if self.vectorize:
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return int8_matmul_rowwise_dequantize(
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X_int8, self.W_int8.t(), state_X, self.state_W, self.bias
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).view(*x.size()[:-1], -1)
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else:
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return int8_matmul_mixed_dequanitze(
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X_int8, self.W_int8.t(), state_X, self.state_W, self.bias
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).view(*x.size()[:-1], -1)
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SwitchBackLinearGlobal = partial(SwitchBackLinear, vectorize=False)
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SwitchBackLinearVectorized = partial(SwitchBackLinear, vectorize=True)
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# This is just the standard linear function.
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class StandardLinearFunction(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input, weight, bias=None):
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@ -1,190 +0,0 @@
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import math
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import torch
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import time
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import triton
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import triton.language as tl
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from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
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tl.libdevice
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# TODO: autotune this better.
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@triton.autotune(
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configs=[
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triton.Config({}, num_stages=1, num_warps=8),
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triton.Config({}, num_stages=2, num_warps=8),
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triton.Config({}, num_stages=4, num_warps=8),
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triton.Config({}, num_stages=8, num_warps=8),
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triton.Config({}, num_stages=1),
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triton.Config({}, num_stages=2),
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triton.Config({}, num_stages=4),
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triton.Config({}, num_stages=8),
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triton.Config({}, num_warps=1),
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triton.Config({}, num_warps=2),
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triton.Config({}, num_warps=4),
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triton.Config({}, num_warps=8),
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],
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key=['n_elements']
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)
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@triton.jit
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def _quantize_rowwise_nogroup_gelu(
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x_ptr,
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output_ptr,
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output_maxs,
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output_fp16,
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n_elements,
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BLOCK_SIZE: tl.constexpr,
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P2: tl.constexpr,
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):
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pid = tl.program_id(axis=0)
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block_start = pid * BLOCK_SIZE
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arange = tl.arange(0, P2)
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offsets = block_start + arange
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row_mask = arange < BLOCK_SIZE
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x = tl.load(x_ptr + offsets, mask=row_mask)
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cdf = 0.5 * (1.0 + tl.libdevice.tanh(x * 0.7978845608 * (1 + 0.044715 * x * x)))
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x_new = x * cdf
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tl.store(output_fp16 + offsets, x_new, mask=row_mask)
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abs_x = tl.abs(x_new)
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max_val = tl.max(tl.where(row_mask, abs_x, 0), axis=0)
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output = tl.libdevice.llrint(127. * (x_new / max_val))
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tl.store(output_ptr + offsets, output, mask=row_mask)
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tl.store(output_maxs + pid, max_val)
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def quantize_rowwise_nogroup_gelu(x: torch.Tensor):
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output = torch.empty(*x.shape, device=x.device, dtype=torch.int8)
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output_fp16 = torch.empty(*x.shape, device=x.device, dtype=torch.float16)
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output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16)
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P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
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assert x.is_cuda and output.is_cuda
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n_elements = output.numel()
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grid = lambda meta: (x.shape[0],)
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_quantize_rowwise_nogroup_gelu[grid](x, output, output_maxs, output_fp16, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
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return output, output_maxs, output_fp16
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# TODO: autotune this better.
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@triton.autotune(
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configs=[
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triton.Config({}, num_stages=1, num_warps=8),
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triton.Config({}, num_stages=2, num_warps=8),
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triton.Config({}, num_stages=4, num_warps=8),
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triton.Config({}, num_stages=8, num_warps=8),
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triton.Config({}, num_stages=1),
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triton.Config({}, num_stages=2),
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triton.Config({}, num_stages=4),
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triton.Config({}, num_stages=8),
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triton.Config({}, num_warps=1),
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triton.Config({}, num_warps=2),
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triton.Config({}, num_warps=4),
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triton.Config({}, num_warps=8),
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],
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key=['n_elements']
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)
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@triton.jit
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def _quantize_rowwise_nogroup_back_gelu(
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x_ptr,
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in_ptr,
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output_ptr,
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output_maxs,
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output_fp16,
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n_elements,
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BLOCK_SIZE: tl.constexpr,
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P2: tl.constexpr,
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):
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pid = tl.program_id(axis=0)
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block_start = pid * BLOCK_SIZE
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arange = tl.arange(0, P2)
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offsets = block_start + arange
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row_mask = arange < BLOCK_SIZE
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x_out = tl.load(x_ptr + offsets, mask=row_mask)
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x_in = tl.load(in_ptr + offsets, mask=row_mask)
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cdf = 0.5 * (1.0 + tl.libdevice.tanh(x_in * 0.7978845608 * (1 + 0.044715 * x_in * x_in)))
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intermediate = tl.libdevice.tanh(x_in * 0.7978845608 * (1 + 0.044715 * x_in * x_in))
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dcdf = 0.5 * (0.7978845608 + 0.1070322243 * x_in * x_in) * (1 - intermediate * intermediate)
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x = x_out * (cdf + x_in * dcdf)
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tl.store(output_fp16 + offsets, x, mask=row_mask)
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abs_x = tl.abs(x)
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max_val = tl.max(tl.where(row_mask, abs_x, 0), axis=0)
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output = tl.libdevice.llrint(127. * (x / max_val))
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tl.store(output_ptr + offsets, output, mask=row_mask)
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tl.store(output_maxs + pid, max_val)
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def quantize_rowwise_nogroup_back_gelu(x: torch.Tensor, y : torch.Tensor):
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output = torch.empty(*x.shape, device=x.device, dtype=torch.int8)
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output_fp16 = torch.empty(*x.shape, device=x.device, dtype=torch.float16)
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output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16)
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P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
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assert x.is_cuda and output.is_cuda
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n_elements = output.numel()
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grid = lambda meta: (x.shape[0],)
|
||||
_quantize_rowwise_nogroup_back_gelu[grid](x, y, output, output_maxs, output_fp16, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
|
||||
return output, output_maxs, output_fp16
|
||||
|
||||
|
||||
|
||||
# if __name__ == '__main__':
|
||||
# torch.manual_seed(0)
|
||||
|
||||
# x = torch.randn(1280, 768).cuda().to(torch.float16)
|
||||
# out = quantize_rowwise_nogroup(x)
|
||||
|
||||
# x_real = (127 * x.float() / x.abs().max(dim=1, keepdim=True)[0]).round().to(torch.int8)
|
||||
# max2 = x.abs().max(1)[0]
|
||||
|
||||
# print(torch.allclose(out[1], max2))
|
||||
# print( (x_real == out[0]).float().mean() )
|
||||
|
||||
# # for i in range(x.shape[0]):
|
||||
# # print( (x_real[i, :] == out[0][i, :]).float().mean() )
|
||||
|
||||
# # print(out[0])
|
||||
# # print(x_real)
|
||||
# # import pdb; pdb.set_trace()
|
||||
# # print(out[2])
|
||||
# # print(out[2][:10])
|
||||
# sums = x.sum(dim=0)
|
||||
# #print(sums[:10])
|
||||
# #print( (sums == out[2]).float().mean() )
|
||||
|
||||
# import pdb; pdb.set_trace()
|
||||
# # import pdb; pdb.set_trace()
|
||||
# # exit()
|
||||
|
||||
# # repeat = 16
|
||||
|
||||
# # for _ in range(8):
|
||||
# # out = quantize_rowwise_nogroup(x)
|
||||
|
||||
# # triton_graph = torch.cuda.CUDAGraph()
|
||||
# # with torch.cuda.graph(triton_graph):
|
||||
# # out = quantize_rowwise_nogroup(x)
|
||||
|
||||
# # triton_graph.replay()
|
||||
|
||||
# # torch.cuda.synchronize()
|
||||
# # start = time.time()
|
||||
# # for _ in range(repeat):
|
||||
# # triton_graph.replay()
|
||||
# # torch.cuda.synchronize()
|
||||
# # end = time.time()
|
||||
|
||||
# # print(out[0])
|
||||
# # print(out[1])
|
||||
# # print(x / x.abs().max(dim=1, keepdim=True)[0])
|
||||
# # max1 = out[1]
|
||||
# # max2 = x.abs().max(1)[0]
|
||||
# # print(max1, max2)
|
||||
# # print(torch.allclose(max1, max2))
|
||||
|
||||
# #print(f"time: {(end - start) / repeat * 1000:.3f} ms")
|
||||
|
|
@ -5,10 +5,14 @@ import triton.language as tl
|
|||
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
|
||||
|
||||
|
||||
# This is a matmul kernel based on triton.ops.matmul
|
||||
# It is modified to support rowwise quantized input and global quantized weight
|
||||
# It's purpose is fused matmul then dequantize
|
||||
# It does support bias.
|
||||
|
||||
def init_to_zero(name):
|
||||
return lambda nargs: nargs[name].zero_()
|
||||
|
||||
|
||||
def get_configs_io_bound():
|
||||
configs = []
|
||||
for num_stages in [2, 3, 4, 5, 6]:
|
||||
|
|
@ -60,130 +64,7 @@ def get_configs_io_bound():
|
|||
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
|
||||
})
|
||||
@triton.jit
|
||||
def _kernel(A, B, C, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl.constexpr,
|
||||
stride_am, stride_ak,
|
||||
stride_bk, stride_bn,
|
||||
stride_cm, stride_cn,
|
||||
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
|
||||
GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
|
||||
ACC_TYPE: tl.constexpr
|
||||
):
|
||||
# matrix multiplication
|
||||
pid = tl.program_id(0)
|
||||
pid_z = tl.program_id(1)
|
||||
grid_m = tl.cdiv(M, BLOCK_M)
|
||||
grid_n = tl.cdiv(N, BLOCK_N)
|
||||
# re-order program ID for better L2 performance
|
||||
width = GROUP_M * grid_n
|
||||
group_id = pid // width
|
||||
group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
|
||||
pid_m = group_id * GROUP_M + (pid % group_size)
|
||||
pid_n = (pid % width) // (group_size)
|
||||
# do matrix multiplication
|
||||
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
||||
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
|
||||
ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
|
||||
rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
|
||||
rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K)
|
||||
# pointers
|
||||
A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
|
||||
B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
|
||||
|
||||
# rematerialize rm and rn to save registers
|
||||
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
||||
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
|
||||
|
||||
w_factor = tl.load(state_w_ptr)
|
||||
x_factor = tl.load(state_x_ptr + ram)[:, None]
|
||||
|
||||
# acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
|
||||
acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32)
|
||||
for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
|
||||
if EVEN_K:
|
||||
a = tl.load(A)
|
||||
b = tl.load(B)
|
||||
else:
|
||||
k_remaining = K - k * (BLOCK_K * SPLIT_K)
|
||||
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
|
||||
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
|
||||
acc += tl.dot(a, b)
|
||||
A += BLOCK_K * SPLIT_K * stride_ak
|
||||
B += BLOCK_K * SPLIT_K * stride_bk
|
||||
|
||||
acc = (w_factor * (x_factor * (acc * divfactor)))
|
||||
acc = acc.to(C.dtype.element_ty)
|
||||
|
||||
C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
|
||||
mask = (rm < M)[:, None] & (rn < N)[None, :]
|
||||
# handles write-back with reduction-splitting
|
||||
if SPLIT_K == 1:
|
||||
tl.store(C, acc, mask=mask)
|
||||
else:
|
||||
tl.atomic_add(C, acc, mask=mask)
|
||||
|
||||
|
||||
def int8_matmul_mixed_dequanitze(a, b, state_x, state_w):
|
||||
device = a.device
|
||||
divfactor = 1. / (127. * 127.)
|
||||
# handle non-contiguous inputs if necessary
|
||||
if a.stride(0) > 1 and a.stride(1) > 1:
|
||||
a = a.contiguous()
|
||||
if b.stride(0) > 1 and b.stride(1) > 1:
|
||||
b = b.contiguous()
|
||||
# checks constraints
|
||||
assert a.shape[1] == b.shape[0], "incompatible dimensions"
|
||||
M, K = a.shape
|
||||
_, N = b.shape
|
||||
# allocates output
|
||||
c = torch.empty((M, N), device=device, dtype=torch.float16)
|
||||
# accumulator types
|
||||
ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
|
||||
# launch kernel
|
||||
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
|
||||
_kernel[grid](a, b, c, state_x, state_w, M, N, K, divfactor,
|
||||
a.stride(0), a.stride(1),
|
||||
b.stride(0), b.stride(1),
|
||||
c.stride(0), c.stride(1),
|
||||
GROUP_M=8, ACC_TYPE=ACC_TYPE)
|
||||
return c
|
||||
|
||||
|
||||
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
# basic configs for compute-bound matmuls
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
|
||||
# good for int8
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
|
||||
] + get_configs_io_bound(),
|
||||
key=['M', 'N', 'K'],
|
||||
prune_configs_by={
|
||||
'early_config_prune': early_config_prune,
|
||||
'perf_model': estimate_matmul_time,
|
||||
'top_k': 10
|
||||
},
|
||||
)
|
||||
@triton.heuristics({
|
||||
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
|
||||
})
|
||||
@triton.jit
|
||||
def _kernel_bias(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl.constexpr, has_bias : tl.constexpr,
|
||||
def _int8_matmul_mixed_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl.constexpr, has_bias : tl.constexpr,
|
||||
stride_am, stride_ak,
|
||||
stride_bk, stride_bn,
|
||||
stride_cm, stride_cn,
|
||||
|
|
@ -236,6 +117,7 @@ def _kernel_bias(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl
|
|||
acc = (w_factor * (x_factor * (acc * divfactor)))
|
||||
acc = acc.to(C.dtype.element_ty)
|
||||
|
||||
# conditionally add bias
|
||||
if has_bias:
|
||||
bias = tl.load(bias + rn).to(C.dtype.element_ty)
|
||||
acc = acc + bias[None, :]
|
||||
|
|
@ -249,7 +131,7 @@ def _kernel_bias(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl
|
|||
tl.atomic_add(C, acc, mask=mask)
|
||||
|
||||
|
||||
def int8_matmul_mixed_dequanitze_bias(a, b, state_x, state_w, bias):
|
||||
def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias):
|
||||
device = a.device
|
||||
divfactor = 1. / (127. * 127.)
|
||||
has_bias = 0 if bias is None else 1
|
||||
|
|
@ -266,9 +148,9 @@ def int8_matmul_mixed_dequanitze_bias(a, b, state_x, state_w, bias):
|
|||
c = torch.empty((M, N), device=device, dtype=torch.float16)
|
||||
# accumulator types
|
||||
ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
|
||||
# launch kernel
|
||||
# launch int8_matmul_mixed_dequantize kernel
|
||||
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
|
||||
_kernel_bias[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
|
||||
_int8_matmul_mixed_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
|
||||
a.stride(0), a.stride(1),
|
||||
b.stride(0), b.stride(1),
|
||||
c.stride(0), c.stride(1),
|
||||
|
|
|
|||
|
|
@ -4,6 +4,10 @@ import triton
|
|||
import triton.language as tl
|
||||
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
|
||||
|
||||
# This is a matmul kernel based on triton.ops.matmul
|
||||
# It is modified to support rowwise quantized input and columnwise quantized weight
|
||||
# It's purpose is fused matmul then dequantize
|
||||
# It does support bias.
|
||||
|
||||
def init_to_zero(name):
|
||||
return lambda nargs: nargs[name].zero_()
|
||||
|
|
@ -60,7 +64,7 @@ def get_configs_io_bound():
|
|||
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
|
||||
})
|
||||
@triton.jit
|
||||
def _kernel(A, B, C, state_x_ptr, state_w_ptr, M, N, K, divfactor,
|
||||
def _int8_matmul_rowwise_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor, has_bias : tl.constexpr,
|
||||
stride_am, stride_ak,
|
||||
stride_bk, stride_bn,
|
||||
stride_cm, stride_cn,
|
||||
|
|
@ -113,6 +117,10 @@ def _kernel(A, B, C, state_x_ptr, state_w_ptr, M, N, K, divfactor,
|
|||
acc = (w_factor * (x_factor * (acc * divfactor)))
|
||||
acc = acc.to(C.dtype.element_ty)
|
||||
|
||||
if has_bias:
|
||||
bias = tl.load(bias + rn).to(C.dtype.element_ty)
|
||||
acc = acc + bias[None, :]
|
||||
|
||||
C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
|
||||
mask = (rm < M)[:, None] & (rn < N)[None, :]
|
||||
# handles write-back with reduction-splitting
|
||||
|
|
@ -122,9 +130,11 @@ def _kernel(A, B, C, state_x_ptr, state_w_ptr, M, N, K, divfactor,
|
|||
tl.atomic_add(C, acc, mask=mask)
|
||||
|
||||
|
||||
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w):
|
||||
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias):
|
||||
divfactor = 1. / (127. * 127.)
|
||||
|
||||
has_bias = 0 if bias is None else 1
|
||||
|
||||
device = a.device
|
||||
# handle non-contiguous inputs if necessary
|
||||
if a.stride(0) > 1 and a.stride(1) > 1:
|
||||
|
|
@ -139,9 +149,9 @@ def int8_matmul_rowwise_dequantize(a, b, state_x, state_w):
|
|||
c = torch.empty((M, N), device=device, dtype=torch.float16)
|
||||
# accumulator types
|
||||
ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
|
||||
# launch kernel
|
||||
# launch int8_matmul_rowwise_dequantize kernel
|
||||
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
|
||||
_kernel[grid](a, b, c, state_x, state_w, M, N, K, divfactor,
|
||||
_int8_matmul_rowwise_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
|
||||
a.stride(0), a.stride(1),
|
||||
b.stride(0), b.stride(1),
|
||||
c.stride(0), c.stride(1),
|
||||
|
|
|
|||
|
|
@ -1,160 +0,0 @@
|
|||
import torch
|
||||
|
||||
import triton
|
||||
import triton.language as tl
|
||||
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
|
||||
|
||||
|
||||
def init_to_zero(name):
|
||||
return lambda nargs: nargs[name].zero_()
|
||||
|
||||
|
||||
def get_configs_io_bound():
|
||||
configs = []
|
||||
for num_stages in [2, 3, 4, 5, 6]:
|
||||
for block_m in [16, 32]:
|
||||
for block_k in [32, 64]:
|
||||
for block_n in [32, 64, 128, 256]:
|
||||
num_warps = 2 if block_n <= 64 else 4
|
||||
configs.append(
|
||||
triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
|
||||
num_stages=num_stages, num_warps=num_warps))
|
||||
# split_k
|
||||
for split_k in [2, 4, 8, 16]:
|
||||
configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
|
||||
num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
|
||||
return configs
|
||||
|
||||
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
# basic configs for compute-bound matmuls
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
|
||||
# good for int8
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
|
||||
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
|
||||
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
|
||||
] + get_configs_io_bound(),
|
||||
key=['M', 'N', 'K'],
|
||||
prune_configs_by={
|
||||
'early_config_prune': early_config_prune,
|
||||
'perf_model': estimate_matmul_time,
|
||||
'top_k': 10
|
||||
},
|
||||
)
|
||||
@triton.heuristics({
|
||||
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
|
||||
})
|
||||
@triton.jit
|
||||
def _kernel(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor, has_bias : tl.constexpr,
|
||||
stride_am, stride_ak,
|
||||
stride_bk, stride_bn,
|
||||
stride_cm, stride_cn,
|
||||
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
|
||||
GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
|
||||
ACC_TYPE: tl.constexpr
|
||||
):
|
||||
# matrix multiplication
|
||||
pid = tl.program_id(0)
|
||||
pid_z = tl.program_id(1)
|
||||
grid_m = tl.cdiv(M, BLOCK_M)
|
||||
grid_n = tl.cdiv(N, BLOCK_N)
|
||||
# re-order program ID for better L2 performance
|
||||
width = GROUP_M * grid_n
|
||||
group_id = pid // width
|
||||
group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
|
||||
pid_m = group_id * GROUP_M + (pid % group_size)
|
||||
pid_n = (pid % width) // (group_size)
|
||||
# do matrix multiplication
|
||||
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
||||
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
|
||||
ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
|
||||
rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
|
||||
rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K)
|
||||
# pointers
|
||||
A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
|
||||
B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
|
||||
|
||||
# rematerialize rm and rn to save registers
|
||||
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
||||
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
|
||||
|
||||
w_factor = tl.load(state_w_ptr + rbn)[None, :]
|
||||
x_factor = tl.load(state_x_ptr + ram)[:, None]
|
||||
|
||||
# acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
|
||||
acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32)
|
||||
for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
|
||||
if EVEN_K:
|
||||
a = tl.load(A)
|
||||
b = tl.load(B)
|
||||
else:
|
||||
k_remaining = K - k * (BLOCK_K * SPLIT_K)
|
||||
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
|
||||
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
|
||||
acc += tl.dot(a, b)
|
||||
A += BLOCK_K * SPLIT_K * stride_ak
|
||||
B += BLOCK_K * SPLIT_K * stride_bk
|
||||
|
||||
acc = (w_factor * (x_factor * (acc * divfactor)))
|
||||
acc = acc.to(C.dtype.element_ty)
|
||||
|
||||
if has_bias:
|
||||
bias = tl.load(bias + rn).to(C.dtype.element_ty)
|
||||
acc = acc + bias[None, :]
|
||||
|
||||
C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
|
||||
mask = (rm < M)[:, None] & (rn < N)[None, :]
|
||||
# handles write-back with reduction-splitting
|
||||
if SPLIT_K == 1:
|
||||
tl.store(C, acc, mask=mask)
|
||||
else:
|
||||
tl.atomic_add(C, acc, mask=mask)
|
||||
|
||||
|
||||
def int8_matmul_rowwise_dequantize_bias(a, b, state_x, state_w, bias):
|
||||
|
||||
#print(bias)
|
||||
divfactor = 1. / (127. * 127.)
|
||||
|
||||
has_bias = 0 if bias is None else 1
|
||||
|
||||
if bias is not None:
|
||||
bias = bias.contiguous()
|
||||
|
||||
device = a.device
|
||||
# handle non-contiguous inputs if necessary
|
||||
if a.stride(0) > 1 and a.stride(1) > 1:
|
||||
a = a.contiguous()
|
||||
if b.stride(0) > 1 and b.stride(1) > 1:
|
||||
b = b.contiguous()
|
||||
# checks constraints
|
||||
assert a.shape[1] == b.shape[0], "incompatible dimensions"
|
||||
M, K = a.shape
|
||||
_, N = b.shape
|
||||
# allocates output
|
||||
c = torch.empty((M, N), device=device, dtype=torch.float16)
|
||||
# accumulator types
|
||||
ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
|
||||
# launch kernel
|
||||
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
|
||||
_kernel[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
|
||||
a.stride(0), a.stride(1),
|
||||
b.stride(0), b.stride(1),
|
||||
c.stride(0), c.stride(1),
|
||||
GROUP_M=8, ACC_TYPE=ACC_TYPE)
|
||||
return c
|
||||
|
|
@ -5,6 +5,8 @@ import triton
|
|||
import triton.language as tl
|
||||
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
|
||||
|
||||
# This kernel does fused columnwise quantization and transpose.
|
||||
|
||||
# TODO: autotune this better.
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
|
|
@ -26,7 +28,7 @@ from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_tim
|
|||
key=['n_elements']
|
||||
)
|
||||
@triton.jit
|
||||
def _quantize_columnwise_nogroup_transpose(
|
||||
def _quantize_columnwise_and_transpose(
|
||||
x_ptr,
|
||||
output_ptr,
|
||||
output_maxs,
|
||||
|
|
@ -51,7 +53,7 @@ def _quantize_columnwise_nogroup_transpose(
|
|||
tl.store(output_ptr + new_offsets, output, mask=p2_arange_mask)
|
||||
tl.store(output_maxs + pid, max_val)
|
||||
|
||||
def quantize_columnwise_nogroup_transpose(x: torch.Tensor):
|
||||
def quantize_columnwise_and_transpose(x: torch.Tensor):
|
||||
M, N = x.shape
|
||||
output = torch.empty(N, M, device=x.device, dtype=torch.int8)
|
||||
output_maxs = torch.empty(x.shape[1], device=x.device, dtype=torch.float16)
|
||||
|
|
@ -61,62 +63,6 @@ def quantize_columnwise_nogroup_transpose(x: torch.Tensor):
|
|||
assert x.is_cuda and output.is_cuda
|
||||
n_elements = output.numel()
|
||||
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
|
||||
_quantize_columnwise_nogroup_transpose[grid](x, output, output_maxs, n_elements, M, N, BLOCK_SIZE=M, P2=P2)
|
||||
_quantize_columnwise_and_transpose[grid](x, output, output_maxs, n_elements, M, N, BLOCK_SIZE=M, P2=P2)
|
||||
return output, output_maxs
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
torch.manual_seed(0)
|
||||
|
||||
x = torch.randn(1280, 768).cuda().to(torch.float16)
|
||||
out = quantize_columnwise_nogroup_transpose(x)
|
||||
|
||||
|
||||
x_real = x.t().float()
|
||||
x_real_int8 = (127. * x_real / x_real.abs().max(dim=1, keepdim=True)[0]).round().to(torch.int8)
|
||||
maxs = x_real.abs().max(dim=1, keepdim=True)[0].half()
|
||||
|
||||
#print(out[0][2,:])
|
||||
|
||||
print((out[0] == x_real_int8).float().mean())
|
||||
print((out[1] == maxs[:, 0]).float().mean())
|
||||
|
||||
# print(out[0])
|
||||
# print(out[1])
|
||||
|
||||
# print(out[0][2,:])
|
||||
# print(x_real[2, :])
|
||||
|
||||
# print((out[0] != x_real).nonzero())
|
||||
|
||||
#import pdb; pdb.set_trace()
|
||||
# repeat = 16
|
||||
|
||||
# for _ in range(8):
|
||||
# out = quantize_columnwise_nogroup_transpose(x)
|
||||
|
||||
# triton_graph = torch.cuda.CUDAGraph()
|
||||
# with torch.cuda.graph(triton_graph):
|
||||
# out = quantize_columnwise_nogroup_transpose(x)
|
||||
|
||||
# triton_graph.replay()
|
||||
|
||||
# torch.cuda.synchronize()
|
||||
# start = time.time()
|
||||
# for _ in range(repeat):
|
||||
# triton_graph.replay()
|
||||
# torch.cuda.synchronize()
|
||||
# end = time.time()
|
||||
|
||||
# print(out[0])
|
||||
# print(out[1])
|
||||
# print(x / x.abs().max(dim=0, keepdim=True)[0])
|
||||
# x_real = (127 * (x / x.abs().max(dim=0, keepdim=True)[0])).round().to(torch.int8)
|
||||
# max1 = out[1]
|
||||
# max2 = x.abs().max(0)[0]
|
||||
# print(max1, max2)
|
||||
# import pdb; pdb.set_trace()
|
||||
# print(torch.allclose(max1, max2))
|
||||
|
||||
# print(f"time: {(end - start) / repeat * 1000:.3f} ms")
|
||||
|
|
@ -5,7 +5,7 @@ import triton
|
|||
import triton.language as tl
|
||||
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
|
||||
|
||||
# TODO: autotune this better.
|
||||
# global quantize
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({'BLOCK_SIZE': 1024,}, num_warps=4),
|
||||
|
|
@ -42,6 +42,7 @@ def quantize_global(x: torch.Tensor):
|
|||
return output, absmax
|
||||
|
||||
|
||||
# global quantize and transpose
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'GROUP_M': 8}, num_warps=4),
|
||||
|
|
@ -97,34 +98,3 @@ def quantize_global_transpose(input):
|
|||
_quantize_global_transpose[grid](input, absmax_inv, out, input.stride(0), input.stride(1), out.stride(0), out.stride(1), M, N)
|
||||
return out, absmax
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
|
||||
w = torch.randn(768, 1280).cuda().to(torch.float16)
|
||||
W_int8, state_w = quantize_global(w)
|
||||
r_state_w = w.abs().max()
|
||||
r_W_int8 = ((127 * w.float()) / state_w).round().to(torch.int8)
|
||||
print((r_W_int8 == W_int8).float().mean())
|
||||
|
||||
# print(r_W_int8)
|
||||
# print(W_int8)
|
||||
exit()
|
||||
repeat = 16
|
||||
|
||||
for _ in range(8):
|
||||
out = quantize_global(w)
|
||||
|
||||
triton_graph = torch.cuda.CUDAGraph()
|
||||
with torch.cuda.graph(triton_graph):
|
||||
out = quantize_global(w)
|
||||
|
||||
triton_graph.replay()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
triton_graph.replay()
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
|
||||
print(f"time: {(end - start) / repeat * 1000:.3f} ms")
|
||||
|
|
|
|||
61
bitsandbytes/nn/triton_utils/v0/quantize_rowwise.py
Normal file
61
bitsandbytes/nn/triton_utils/v0/quantize_rowwise.py
Normal file
|
|
@ -0,0 +1,61 @@
|
|||
import math
|
||||
import torch
|
||||
import time
|
||||
import triton
|
||||
import triton.language as tl
|
||||
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
|
||||
|
||||
# rowwise quantize
|
||||
|
||||
# TODO: autotune this better.
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({}, num_stages=1, num_warps=8),
|
||||
triton.Config({}, num_stages=2, num_warps=8),
|
||||
triton.Config({}, num_stages=4, num_warps=8),
|
||||
triton.Config({}, num_stages=8, num_warps=8),
|
||||
triton.Config({}, num_stages=1),
|
||||
triton.Config({}, num_stages=2),
|
||||
triton.Config({}, num_stages=4),
|
||||
triton.Config({}, num_stages=8),
|
||||
triton.Config({}, num_warps=1),
|
||||
triton.Config({}, num_warps=2),
|
||||
triton.Config({}, num_warps=4),
|
||||
triton.Config({}, num_warps=8),
|
||||
],
|
||||
key=['n_elements']
|
||||
)
|
||||
@triton.jit
|
||||
def _quantize_rowwise(
|
||||
x_ptr,
|
||||
output_ptr,
|
||||
output_maxs,
|
||||
n_elements,
|
||||
BLOCK_SIZE: tl.constexpr,
|
||||
P2: tl.constexpr,
|
||||
):
|
||||
pid = tl.program_id(axis=0)
|
||||
block_start = pid * BLOCK_SIZE
|
||||
arange = tl.arange(0, P2)
|
||||
offsets = block_start + arange
|
||||
row_mask = arange < BLOCK_SIZE
|
||||
x = tl.load(x_ptr + offsets, mask=row_mask)
|
||||
|
||||
abs_x = tl.abs(x)
|
||||
max_val = tl.max(tl.where(row_mask, abs_x, 0), axis=0)
|
||||
output = tl.libdevice.llrint(127. * (x / max_val))
|
||||
tl.store(output_ptr + offsets, output, mask=row_mask)
|
||||
tl.store(output_maxs + pid, max_val)
|
||||
|
||||
def quantize_rowwise(x: torch.Tensor):
|
||||
output = torch.empty(*x.shape, device=x.device, dtype=torch.int8)
|
||||
output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16)
|
||||
|
||||
P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
|
||||
|
||||
assert x.is_cuda and output.is_cuda
|
||||
n_elements = output.numel()
|
||||
grid = lambda meta: (x.shape[0],)
|
||||
_quantize_rowwise[grid](x, output, output_maxs, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
|
||||
return output, output_maxs
|
||||
|
||||
|
|
@ -1,174 +0,0 @@
|
|||
import math
|
||||
import torch
|
||||
import time
|
||||
import triton
|
||||
import triton.language as tl
|
||||
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
|
||||
|
||||
# TODO: autotune this better.
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({}, num_stages=1, num_warps=8),
|
||||
triton.Config({}, num_stages=2, num_warps=8),
|
||||
triton.Config({}, num_stages=4, num_warps=8),
|
||||
triton.Config({}, num_stages=8, num_warps=8),
|
||||
triton.Config({}, num_stages=1),
|
||||
triton.Config({}, num_stages=2),
|
||||
triton.Config({}, num_stages=4),
|
||||
triton.Config({}, num_stages=8),
|
||||
triton.Config({}, num_warps=1),
|
||||
triton.Config({}, num_warps=2),
|
||||
triton.Config({}, num_warps=4),
|
||||
triton.Config({}, num_warps=8),
|
||||
],
|
||||
key=['n_elements']
|
||||
)
|
||||
@triton.jit
|
||||
def _quantize_rowwise_nogroup(
|
||||
x_ptr,
|
||||
output_ptr,
|
||||
output_maxs,
|
||||
n_elements,
|
||||
BLOCK_SIZE: tl.constexpr,
|
||||
P2: tl.constexpr,
|
||||
):
|
||||
pid = tl.program_id(axis=0)
|
||||
block_start = pid * BLOCK_SIZE
|
||||
arange = tl.arange(0, P2)
|
||||
offsets = block_start + arange
|
||||
row_mask = arange < BLOCK_SIZE
|
||||
x = tl.load(x_ptr + offsets, mask=row_mask)
|
||||
|
||||
abs_x = tl.abs(x)
|
||||
max_val = tl.max(tl.where(row_mask, abs_x, 0), axis=0)
|
||||
output = tl.libdevice.llrint(127. * (x / max_val))
|
||||
tl.store(output_ptr + offsets, output, mask=row_mask)
|
||||
tl.store(output_maxs + pid, max_val)
|
||||
|
||||
def quantize_rowwise_nogroup(x: torch.Tensor):
|
||||
output = torch.empty(*x.shape, device=x.device, dtype=torch.int8)
|
||||
output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16)
|
||||
|
||||
P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
|
||||
|
||||
assert x.is_cuda and output.is_cuda
|
||||
n_elements = output.numel()
|
||||
grid = lambda meta: (x.shape[0],)
|
||||
_quantize_rowwise_nogroup[grid](x, output, output_maxs, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
|
||||
return output, output_maxs
|
||||
|
||||
|
||||
@triton.autotune(
|
||||
configs=[
|
||||
triton.Config({}, num_warps=1),
|
||||
triton.Config({}, num_warps=2),
|
||||
triton.Config({}, num_warps=4),
|
||||
triton.Config({}, num_warps=8),
|
||||
],
|
||||
key=['n_elements']
|
||||
)
|
||||
@triton.jit
|
||||
def _experimental_quantize_rowwise_nogroup(
|
||||
x_ptr,
|
||||
output_ptr,
|
||||
bias_grad_ptr,
|
||||
output_maxs,
|
||||
n_elements,
|
||||
M: tl.constexpr, N: tl.constexpr,
|
||||
BLOCK_SIZE: tl.constexpr,
|
||||
P2: tl.constexpr,
|
||||
P2M: tl.constexpr,
|
||||
):
|
||||
pid = tl.program_id(axis=0)
|
||||
if pid < M:
|
||||
block_start = pid * BLOCK_SIZE
|
||||
arange = tl.arange(0, P2)
|
||||
offsets = block_start + arange
|
||||
row_mask = arange < BLOCK_SIZE
|
||||
x = tl.load(x_ptr + offsets, mask=row_mask)
|
||||
|
||||
abs_x = tl.abs(x)
|
||||
max_val = tl.max(tl.where(row_mask, abs_x, 0), axis=0)
|
||||
output = tl.libdevice.llrint(127. * (x / max_val))
|
||||
tl.store(output_ptr + offsets, output, mask=row_mask)
|
||||
tl.store(output_maxs + pid, max_val)
|
||||
else:
|
||||
real_pid = pid - M
|
||||
arange_new = tl.arange(0, P2M)
|
||||
mask_new = arange_new < M
|
||||
offsets_new = real_pid + arange_new * N
|
||||
new_x = tl.load(x_ptr + offsets_new, mask=mask_new)
|
||||
s = tl.sum(tl.where(mask_new, new_x, 0).to(tl.float32), axis=0)
|
||||
tl.store(bias_grad_ptr + real_pid, s)
|
||||
|
||||
def experimental_quantize_rowwise_nogroup(x: torch.Tensor):
|
||||
M, N = x.shape
|
||||
output = torch.empty(*x.shape, device=x.device, dtype=torch.int8)
|
||||
output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16)
|
||||
bias_grad = torch.empty(x.shape[1], device=x.device, dtype=torch.float16)
|
||||
|
||||
P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
|
||||
P2M = int(2 ** (math.ceil(math.log2(x.shape[0]))))
|
||||
|
||||
assert x.is_cuda and output.is_cuda
|
||||
n_elements = output.numel()
|
||||
grid = lambda meta: (x.shape[0] + x.shape[1],)
|
||||
_experimental_quantize_rowwise_nogroup[grid](x, output, bias_grad, output_maxs, n_elements, M, N, BLOCK_SIZE=x.shape[1], P2=P2, P2M=P2M)
|
||||
return output, output_maxs, bias_grad
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
torch.manual_seed(0)
|
||||
|
||||
x = torch.randn(1280, 768).cuda().to(torch.float16)
|
||||
out = quantize_rowwise_nogroup(x)
|
||||
|
||||
x_real = (127 * x.float() / x.abs().max(dim=1, keepdim=True)[0]).round().to(torch.int8)
|
||||
max2 = x.abs().max(1)[0]
|
||||
|
||||
print(torch.allclose(out[1], max2))
|
||||
print( (x_real == out[0]).float().mean() )
|
||||
|
||||
# for i in range(x.shape[0]):
|
||||
# print( (x_real[i, :] == out[0][i, :]).float().mean() )
|
||||
|
||||
# print(out[0])
|
||||
# print(x_real)
|
||||
# import pdb; pdb.set_trace()
|
||||
# print(out[2])
|
||||
# print(out[2][:10])
|
||||
sums = x.sum(dim=0)
|
||||
#print(sums[:10])
|
||||
#print( (sums == out[2]).float().mean() )
|
||||
|
||||
import pdb; pdb.set_trace()
|
||||
# import pdb; pdb.set_trace()
|
||||
# exit()
|
||||
|
||||
# repeat = 16
|
||||
|
||||
# for _ in range(8):
|
||||
# out = quantize_rowwise_nogroup(x)
|
||||
|
||||
# triton_graph = torch.cuda.CUDAGraph()
|
||||
# with torch.cuda.graph(triton_graph):
|
||||
# out = quantize_rowwise_nogroup(x)
|
||||
|
||||
# triton_graph.replay()
|
||||
|
||||
# torch.cuda.synchronize()
|
||||
# start = time.time()
|
||||
# for _ in range(repeat):
|
||||
# triton_graph.replay()
|
||||
# torch.cuda.synchronize()
|
||||
# end = time.time()
|
||||
|
||||
# print(out[0])
|
||||
# print(out[1])
|
||||
# print(x / x.abs().max(dim=1, keepdim=True)[0])
|
||||
# max1 = out[1]
|
||||
# max2 = x.abs().max(1)[0]
|
||||
# print(max1, max2)
|
||||
# print(torch.allclose(max1, max2))
|
||||
|
||||
#print(f"time: {(end - start) / repeat * 1000:.3f} ms")
|
||||
60
speed_benchmark/info_a100_py2.jsonl
Normal file
60
speed_benchmark/info_a100_py2.jsonl
Normal file
|
|
@ -0,0 +1,60 @@
|
|||
{"repeat": 64, "batch_size": 8192, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 0.28139352798461914, "standard_gw": 0.2811811864376068, "standard_gx": 0.30258670449256897, "rowwise_fwd": 0.1994594931602478, "rowwise_bwd": 0.16159191727638245, "global_fwd": 0.19502267241477966, "global_bwd": 0.16080215573310852, "x_quantize_rowwise": 0.03306940197944641, "g_quantize_rowwise": 0.08210167288780212, "w_quantize_rowwise": 0.03385916352272034, "w_quantize_colwise_transpose": 0.08635595440864563, "w_quantize_global": 0.09237229824066162, "w_quantize_global_transpose": 0.10007619857788086, "time_standard": 0.8651614189147949, "time_rowwise": 0.8776187896728516, "time_global": 0.944625586271286}
|
||||
{"repeat": 64, "batch_size": 8192, "dim_out": 1024, "dim_in": 4096, "wm": 4, "switch": true, "standard_fwd": 0.262625515460968, "standard_gw": 0.2806223928928375, "standard_gx": 0.31118839979171753, "rowwise_fwd": 0.1828707754611969, "rowwise_bwd": 0.21236762404441833, "global_fwd": 0.16665831208229065, "global_bwd": 0.19929558038711548, "x_quantize_rowwise": 0.08227676153182983, "g_quantize_rowwise": 0.03310292959213257, "w_quantize_rowwise": 0.032648444175720215, "w_quantize_colwise_transpose": 0.09015202522277832, "w_quantize_global": 0.0988692045211792, "w_quantize_global_transpose": 0.10057538747787476, "time_standard": 0.8544363081455231, "time_rowwise": 0.9140409529209137, "time_global": 0.96140056848526}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 0.5731917917728424, "standard_gw": 0.5709454417228699, "standard_gx": 0.5963630974292755, "rowwise_fwd": 0.37662312388420105, "rowwise_bwd": 0.281747430562973, "global_fwd": 0.36768242716789246, "global_bwd": 0.28043612837791443, "x_quantize_rowwise": 0.046547502279281616, "g_quantize_rowwise": 0.15532970428466797, "w_quantize_rowwise": 0.032436102628707886, "w_quantize_colwise_transpose": 0.08635222911834717, "w_quantize_global": 0.0947415828704834, "w_quantize_global_transpose": 0.10129809379577637, "time_standard": 1.7405003309249878, "time_rowwise": 1.5499815344810486, "time_global": 1.616980880498886}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 1024, "dim_in": 4096, "wm": 4, "switch": true, "standard_fwd": 0.5341619253158569, "standard_gw": 0.5690865218639374, "standard_gx": 0.599835067987442, "rowwise_fwd": 0.3233291208744049, "rowwise_bwd": 0.41359663009643555, "global_fwd": 0.2831108868122101, "global_bwd": 0.37280842661857605, "x_quantize_rowwise": 0.15563145279884338, "g_quantize_rowwise": 0.046741217374801636, "w_quantize_rowwise": 0.03306940197944641, "w_quantize_colwise_transpose": 0.09020790457725525, "w_quantize_global": 0.0925213098526001, "w_quantize_global_transpose": 0.09945780038833618, "time_standard": 1.7030835151672363, "time_rowwise": 1.6316622495651245, "time_global": 1.6193576157093048}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 1.2199915945529938, "standard_gw": 1.1069811880588531, "standard_gx": 1.09761580824852, "rowwise_fwd": 0.738043338060379, "rowwise_bwd": 0.5549229681491852, "global_fwd": 0.7219798862934113, "global_bwd": 0.5512163043022156, "x_quantize_rowwise": 0.08748471736907959, "g_quantize_rowwise": 0.3023110330104828, "w_quantize_rowwise": 0.03182142972946167, "w_quantize_colwise_transpose": 0.08632615208625793, "w_quantize_global": 0.09445473551750183, "w_quantize_global_transpose": 0.10032951831817627, "time_standard": 3.424588590860367, "time_rowwise": 2.9078908264636993, "time_global": 2.9647573828697205}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 1024, "dim_in": 4096, "wm": 4, "switch": true, "standard_fwd": 1.1040829122066498, "standard_gw": 1.1221766471862793, "standard_gx": 1.1548101902008057, "rowwise_fwd": 0.581938773393631, "rowwise_bwd": 0.7480122148990631, "global_fwd": 0.5537159740924835, "global_bwd": 0.7232688367366791, "x_quantize_rowwise": 0.30193477869033813, "g_quantize_rowwise": 0.08745118975639343, "w_quantize_rowwise": 0.03374740481376648, "w_quantize_colwise_transpose": 0.09068101644515991, "w_quantize_global": 0.09645149111747742, "w_quantize_global_transpose": 0.10189786553382874, "time_standard": 3.3810697495937347, "time_rowwise": 2.9659420251846313, "time_global": 2.9868967831134796}
|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 2.4533793330192566, "standard_gw": 2.1938569843769073, "standard_gx": 2.179361879825592, "rowwise_fwd": 1.4615543186664581, "rowwise_bwd": 1.0522231459617615, "global_fwd": 1.4288239181041718, "global_bwd": 1.0450035333633423, "x_quantize_rowwise": 0.1691766083240509, "g_quantize_rowwise": 0.5951300263404846, "w_quantize_rowwise": 0.03337860107421875, "w_quantize_colwise_transpose": 0.08653849363327026, "w_quantize_global": 0.0940859317779541, "w_quantize_global_transpose": 0.09976327419281006, "time_standard": 6.826598197221756, "time_rowwise": 5.5918581783771515, "time_global": 5.625840276479721}
|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 1024, "dim_in": 4096, "wm": 4, "switch": true, "standard_fwd": 2.1698065102100372, "standard_gw": 2.1875128149986267, "standard_gx": 2.2887587547302246, "rowwise_fwd": 1.0762326419353485, "rowwise_bwd": 1.4638006687164307, "global_fwd": 1.0450668632984161, "global_bwd": 1.4308765530586243, "x_quantize_rowwise": 0.5953535437583923, "g_quantize_rowwise": 0.16899779438972473, "w_quantize_rowwise": 0.03240257501602173, "w_quantize_colwise_transpose": 0.09106099605560303, "w_quantize_global": 0.09546056389808655, "w_quantize_global_transpose": 0.09852275252342224, "time_standard": 6.6460780799388885, "time_rowwise": 5.615361034870148, "time_global": 5.621790885925293}
|
||||
{"repeat": 64, "batch_size": 131072, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 4.858218133449554, "standard_gw": 4.3631307780742645, "standard_gx": 4.404045641422272, "rowwise_fwd": 2.9063820838928223, "rowwise_bwd": 2.094462513923645, "global_fwd": 2.8426870703697205, "global_bwd": 2.0792782306671143, "x_quantize_rowwise": 0.33241137862205505, "g_quantize_rowwise": 1.1817105114459991, "w_quantize_rowwise": 0.03374367952346802, "w_quantize_colwise_transpose": 0.08633732795715332, "w_quantize_global": 0.09231641888618469, "w_quantize_global_transpose": 0.100012868642807, "time_standard": 13.62539455294609, "time_rowwise": 10.998178273439407, "time_global": 10.991547256708145}
|
||||
{"repeat": 64, "batch_size": 131072, "dim_out": 1024, "dim_in": 4096, "wm": 4, "switch": true, "standard_fwd": 4.246581345796585, "standard_gw": 4.42587211728096, "standard_gx": 4.581417888402939, "rowwise_fwd": 2.1114833652973175, "rowwise_bwd": 2.9050447046756744, "global_fwd": 2.0806826651096344, "global_bwd": 2.85966694355011, "x_quantize_rowwise": 1.1816024780273438, "g_quantize_rowwise": 0.33330172300338745, "w_quantize_rowwise": 0.033445656299591064, "w_quantize_colwise_transpose": 0.09065866470336914, "w_quantize_global": 0.09239837527275085, "w_quantize_global_transpose": 0.09984523057937622, "time_standard": 13.253871351480484, "time_rowwise": 11.081408709287643, "time_global": 11.073369532823563}
|
||||
{"repeat": 64, "batch_size": 8192, "dim_out": 5120, "dim_in": 1280, "wm": 4, "switch": false, "standard_fwd": 0.4859529435634613, "standard_gw": 0.46338513493537903, "standard_gx": 0.42321905493736267, "rowwise_fwd": 0.2761557698249817, "rowwise_bwd": 0.20775198936462402, "global_fwd": 0.2713911235332489, "global_bwd": 0.20639970898628235, "x_quantize_rowwise": 0.033095479011535645, "g_quantize_rowwise": 0.11894106864929199, "w_quantize_rowwise": 0.03125518560409546, "w_quantize_colwise_transpose": 0.1424551010131836, "w_quantize_global": 0.07288157939910889, "w_quantize_global_transpose": 0.08071959018707275, "time_standard": 1.372557133436203, "time_rowwise": 1.2730397284030914, "time_global": 1.2468136847019196}
|
||||
{"repeat": 64, "batch_size": 8192, "dim_out": 1280, "dim_in": 5120, "wm": 4, "switch": true, "standard_fwd": 0.3920421004295349, "standard_gw": 0.44424086809158325, "standard_gx": 0.4759356379508972, "rowwise_fwd": 0.23231282830238342, "rowwise_bwd": 0.28430670499801636, "global_fwd": 0.20883232355117798, "global_bwd": 0.2741999924182892, "x_quantize_rowwise": 0.12018159031867981, "g_quantize_rowwise": 0.03195926547050476, "w_quantize_rowwise": 0.026017427444458008, "w_quantize_colwise_transpose": 0.14733895659446716, "w_quantize_global": 0.07734447717666626, "w_quantize_global_transpose": 0.0788569450378418, "time_standard": 1.3122186064720154, "time_rowwise": 1.2863576412200928, "time_global": 1.235615462064743}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 5120, "dim_in": 1280, "wm": 4, "switch": false, "standard_fwd": 1.0111741721630096, "standard_gw": 0.9267590939998627, "standard_gx": 0.8254274725914001, "rowwise_fwd": 0.5434826016426086, "rowwise_bwd": 0.4077926278114319, "global_fwd": 0.5318708717823029, "global_bwd": 0.40537863969802856, "x_quantize_rowwise": 0.059738755226135254, "g_quantize_rowwise": 0.2299174666404724, "w_quantize_rowwise": 0.02545863389968872, "w_quantize_colwise_transpose": 0.14269724488258362, "w_quantize_global": 0.07300823926925659, "w_quantize_global_transpose": 0.07878988981246948, "time_standard": 2.7633607387542725, "time_rowwise": 2.335846424102783, "time_global": 2.305462956428528}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 1280, "dim_in": 5120, "wm": 4, "switch": true, "standard_fwd": 0.8095316588878632, "standard_gw": 0.8607134222984314, "standard_gx": 0.9204968810081482, "rowwise_fwd": 0.4275888204574585, "rowwise_bwd": 0.5485899746417999, "global_fwd": 0.41000545024871826, "global_bwd": 0.5317628383636475, "x_quantize_rowwise": 0.2301819622516632, "g_quantize_rowwise": 0.059254467487335205, "w_quantize_rowwise": 0.02466142177581787, "w_quantize_colwise_transpose": 0.14865398406982422, "w_quantize_global": 0.07582828402519226, "w_quantize_global_transpose": 0.08231401443481445, "time_standard": 2.5907419621944427, "time_rowwise": 2.2996440529823303, "time_global": 2.2500604391098022}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 5120, "dim_in": 1280, "wm": 4, "switch": false, "standard_fwd": 2.0658522844314575, "standard_gw": 1.718364655971527, "standard_gx": 1.6660578548908234, "rowwise_fwd": 1.066897064447403, "rowwise_bwd": 0.8070804178714752, "global_fwd": 1.0473169386386871, "global_bwd": 0.8021742105484009, "x_quantize_rowwise": 0.11274218559265137, "g_quantize_rowwise": 0.4518181085586548, "w_quantize_rowwise": 0.026501715183258057, "w_quantize_colwise_transpose": 0.14259666204452515, "w_quantize_global": 0.07484853267669678, "w_quantize_global_transpose": 0.07976219058036804, "time_standard": 5.450274795293808, "time_rowwise": 4.326000809669495, "time_global": 4.287026822566986}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 1280, "dim_in": 5120, "wm": 4, "switch": true, "standard_fwd": 2.7549192309379578, "standard_gw": 1.6954988241195679, "standard_gx": 1.8179528415203094, "rowwise_fwd": 0.8649080991744995, "rowwise_bwd": 1.0746456682682037, "global_fwd": 0.8023083209991455, "global_bwd": 1.0471977293491364, "x_quantize_rowwise": 0.45225024223327637, "g_quantize_rowwise": 0.11286512017250061, "w_quantize_rowwise": 0.0252649188041687, "w_quantize_colwise_transpose": 0.14732033014297485, "w_quantize_global": 0.07537379860877991, "w_quantize_global_transpose": 0.0807642936706543, "time_standard": 6.268370896577835, "time_rowwise": 4.372753202915192, "time_global": 4.266258329153061}
|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 5120, "dim_in": 1280, "wm": 4, "switch": false, "standard_fwd": 4.098430275917053, "standard_gw": 3.3501461148262024, "standard_gx": 5.560480058193207, "rowwise_fwd": 2.112947404384613, "rowwise_bwd": 1.605246216058731, "global_fwd": 2.0697638392448425, "global_bwd": 1.5953518450260162, "x_quantize_rowwise": 0.21921470761299133, "g_quantize_rowwise": 0.8956789970397949, "w_quantize_rowwise": 0.02710893750190735, "w_quantize_colwise_transpose": 0.14268234372138977, "w_quantize_global": 0.07259473204612732, "w_quantize_global_transpose": 0.07899105548858643, "time_standard": 13.009056448936462, "time_rowwise": 8.35302472114563, "time_global": 8.281741291284561}
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
{"repeat": 64, "batch_size": 8192, "dim_out": 2048, "dim_in": 8192, "wm": 4, "switch": true, "standard_fwd": 1.0731369256973267, "standard_gw": 1.1365897953510284, "standard_gx": 1.1498592793941498, "rowwise_fwd": 0.5573518574237823, "rowwise_bwd": 0.615488737821579, "global_fwd": 0.5220361053943634, "global_bwd": 0.5939789116382599, "x_quantize_rowwise": 0.15765801072120667, "g_quantize_rowwise": 0.04369020462036133, "w_quantize_rowwise": 0.047359615564346313, "w_quantize_colwise_transpose": 0.5526281893253326, "w_quantize_global": 0.13606995344161987, "w_quantize_global_transpose": 0.15017390251159668, "time_standard": 3.359586000442505, "time_rowwise": 3.1107664108276367, "time_global": 2.7401968836784363}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 8192, "dim_in": 2048, "wm": 4, "switch": false, "standard_fwd": 2.4274885654449463, "standard_gw": 2.1799951791763306, "standard_gx": 2.1426528692245483, "rowwise_fwd": 1.195710152387619, "rowwise_bwd": 1.027170568704605, "global_fwd": 1.1747106909751892, "global_bwd": 1.0251589119434357, "x_quantize_rowwise": 0.08098781108856201, "g_quantize_rowwise": 0.3052949905395508, "w_quantize_rowwise": 0.043764710426330566, "w_quantize_colwise_transpose": 0.33987686038017273, "w_quantize_global": 0.13646483421325684, "w_quantize_global_transpose": 0.14739856123924255, "time_standard": 6.750136613845825, "time_rowwise": 5.172800272703171, "time_global": 5.050010979175568}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 2048, "dim_in": 8192, "wm": 4, "switch": true, "standard_fwd": 2.1661892533302307, "standard_gw": 2.0948275923728943, "standard_gx": 2.306375652551651, "rowwise_fwd": 1.0587647557258606, "rowwise_bwd": 1.1999905109405518, "global_fwd": 1.0296404361724854, "global_bwd": 1.1749230325222015, "x_quantize_rowwise": 0.3054030239582062, "g_quantize_rowwise": 0.08077546954154968, "w_quantize_rowwise": 0.047225505113601685, "w_quantize_colwise_transpose": 0.600133091211319, "w_quantize_global": 0.13613328337669373, "w_quantize_global_transpose": 0.1484006643295288, "time_standard": 6.567392498254776, "time_rowwise": 5.387119948863983, "time_global": 4.97010350227356}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 8192, "dim_in": 2048, "wm": 4, "switch": false, "standard_fwd": 4.807606339454651, "standard_gw": 4.170913249254227, "standard_gx": 4.117622971534729, "rowwise_fwd": 2.370934933423996, "rowwise_bwd": 1.9481778144836426, "global_fwd": 2.3383721709251404, "global_bwd": 1.9443817436695099, "x_quantize_rowwise": 0.1547597348690033, "g_quantize_rowwise": 0.6000511348247528, "w_quantize_rowwise": 0.04361942410469055, "w_quantize_colwise_transpose": 0.3403201699256897, "w_quantize_global": 0.13600289821624756, "w_quantize_global_transpose": 0.1474134624004364, "time_standard": 13.096142560243607, "time_rowwise": 9.628776460886002, "time_global": 9.491894394159317}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 2048, "dim_in": 8192, "wm": 4, "switch": true, "standard_fwd": 4.1619837284088135, "standard_gw": 4.181284457445145, "standard_gx": 4.635505378246307, "rowwise_fwd": 1.9684135913848877, "rowwise_bwd": 2.3750364780426025, "global_fwd": 1.9445866346359253, "global_bwd": 2.3551955819129944, "x_quantize_rowwise": 0.6004162132740021, "g_quantize_rowwise": 0.15468522906303406, "w_quantize_rowwise": 0.04730746150016785, "w_quantize_colwise_transpose": 0.5999617278575897, "w_quantize_global": 0.1364201307296753, "w_quantize_global_transpose": 0.14847144484519958, "time_standard": 12.978773564100266, "time_rowwise": 9.927105158567429, "time_global": 9.521059691905975}
|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 8192, "dim_in": 2048, "wm": 4, "switch": false, "standard_fwd": 9.52371209859848, "standard_gw": 8.354485034942627, "standard_gx": 8.69860127568245, "rowwise_fwd": 4.717472940683365, "rowwise_bwd": 3.8843750953674316, "global_fwd": 4.645414650440216, "global_bwd": 3.8761012256145477, "x_quantize_rowwise": 0.3024861216545105, "g_quantize_rowwise": 1.1897757649421692, "w_quantize_rowwise": 0.04366785287857056, "w_quantize_colwise_transpose": 0.33988431096076965, "w_quantize_global": 0.1359507441520691, "w_quantize_global_transpose": 0.14724582433700562, "time_standard": 26.576798409223557, "time_rowwise": 18.832147121429443, "time_global": 18.651459366083145}
|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 2048, "dim_in": 8192, "wm": 4, "switch": true, "standard_fwd": 8.307881653308868, "standard_gw": 8.214320987462997, "standard_gx": 9.21182706952095, "rowwise_fwd": 3.8919784128665924, "rowwise_bwd": 4.72346693277359, "global_fwd": 3.8761794567108154, "global_bwd": 4.673641175031662, "x_quantize_rowwise": 1.1893920600414276, "g_quantize_rowwise": 0.3024972975254059, "w_quantize_rowwise": 0.04708021879196167, "w_quantize_colwise_transpose": 0.6039328873157501, "w_quantize_global": 0.13624504208564758, "w_quantize_global_transpose": 0.14867261052131653, "time_standard": 25.734029710292816, "time_rowwise": 18.972668796777725, "time_global": 18.540948629379272}
|
||||
{"repeat": 64, "batch_size": 131072, "dim_out": 8192, "dim_in": 2048, "wm": 4, "switch": false, "standard_fwd": 19.30372044444084, "standard_gw": 16.480475664138794, "standard_gx": 17.61433482170105, "rowwise_fwd": 9.49602946639061, "rowwise_bwd": 7.768530398607254, "global_fwd": 9.3533955514431, "global_bwd": 7.749464362859726, "x_quantize_rowwise": 0.5977451801300049, "g_quantize_rowwise": 2.3684948682785034, "w_quantize_rowwise": 0.04375725984573364, "w_quantize_colwise_transpose": 0.34042075276374817, "w_quantize_global": 0.13628974556922913, "w_quantize_global_transpose": 0.14671683311462402, "time_standard": 53.398530930280685, "time_rowwise": 37.09545359015465, "time_global": 36.83258220553398}
|
||||
{"repeat": 64, "batch_size": 131072, "dim_out": 2048, "dim_in": 8192, "wm": 4, "switch": true, "standard_fwd": 18.041003495454788, "standard_gw": 17.770148813724518, "standard_gx": 17.70009845495224, "rowwise_fwd": 7.756810635328293, "rowwise_bwd": 9.502101689577103, "global_fwd": 7.7384114265441895, "global_bwd": 9.36170294880867, "x_quantize_rowwise": 2.3686252534389496, "g_quantize_rowwise": 0.5980581045150757, "w_quantize_rowwise": 0.04723668098449707, "w_quantize_colwise_transpose": 0.6035342812538147, "w_quantize_global": 0.13603642582893372, "w_quantize_global_transpose": 0.1485198736190796, "time_standard": 53.511250764131546, "time_rowwise": 38.64651545882225, "time_global": 38.121502846479416}
|
||||
{"repeat": 64, "batch_size": 8192, "dim_out": 16384, "dim_in": 4096, "wm": 4, "switch": false, "standard_fwd": 4.598241299390793, "standard_gw": 4.294309765100479, "standard_gx": 4.261095076799393, "rowwise_fwd": 2.0976848900318146, "rowwise_bwd": 1.9718967378139496, "global_fwd": 2.0763762295246124, "global_bwd": 1.9703581929206848, "x_quantize_rowwise": 0.08216872811317444, "g_quantize_rowwise": 0.4405900835990906, "w_quantize_rowwise": 0.1553371548652649, "w_quantize_colwise_transpose": 1.6110725700855255, "w_quantize_global": 0.481240451335907, "w_quantize_global_transpose": 0.5061514675617218, "time_standard": 13.153646141290665, "time_rowwise": 10.653059929609299, "time_global": 9.85119491815567}
|
||||
{"repeat": 64, "batch_size": 8192, "dim_out": 4096, "dim_in": 16384, "wm": 4, "switch": true, "standard_fwd": 4.35885414481163, "standard_gw": 4.29583340883255, "standard_gx": 4.5370906591415405, "rowwise_fwd": 2.0015686750411987, "rowwise_bwd": 2.097565680742264, "global_fwd": 1.969795674085617, "global_bwd": 2.075403928756714, "x_quantize_rowwise": 0.43984130024909973, "g_quantize_rowwise": 0.08216127753257751, "w_quantize_rowwise": 0.22544339299201965, "w_quantize_colwise_transpose": 2.4342015385627747, "w_quantize_global": 0.48087164759635925, "w_quantize_global_transpose": 0.5099289119243622, "time_standard": 13.19177821278572, "time_rowwise": 11.576615273952484, "time_global": 9.85383614897728}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 16384, "dim_in": 4096, "wm": 4, "switch": false, "standard_fwd": 9.09888744354248, "standard_gw": 8.230950683355331, "standard_gx": 8.465446531772614, "rowwise_fwd": 4.182614386081696, "rowwise_bwd": 3.747660666704178, "global_fwd": 4.138719290494919, "global_bwd": 3.74777615070343, "x_quantize_rowwise": 0.15515834093093872, "g_quantize_rowwise": 0.8699297904968262, "w_quantize_rowwise": 0.15544891357421875, "w_quantize_colwise_transpose": 1.6132444143295288, "w_quantize_global": 0.48100948333740234, "w_quantize_global_transpose": 0.5051903426647186, "time_standard": 25.795284658670425, "time_rowwise": 18.955007195472717, "time_global": 18.128734081983566}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 4096, "dim_in": 16384, "wm": 4, "switch": true, "standard_fwd": 8.378107100725174, "standard_gw": 8.923027664422989, "standard_gx": 9.049762040376663, "rowwise_fwd": 3.765825182199478, "rowwise_bwd": 4.183519631624222, "global_fwd": 3.744799643754959, "global_bwd": 4.1590481996536255, "x_quantize_rowwise": 0.8693933486938477, "g_quantize_rowwise": 0.1553073525428772, "w_quantize_rowwise": 0.2258792519569397, "w_quantize_colwise_transpose": 2.4386271834373474, "w_quantize_global": 0.4811100661754608, "w_quantize_global_transpose": 0.5102269351482391, "time_standard": 26.350896805524826, "time_rowwise": 20.5615796148777, "time_global": 18.842913210392}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 16384, "dim_in": 4096, "wm": 4, "switch": false, "standard_fwd": 18.266115337610245, "standard_gw": 17.671160399913788, "standard_gx": 17.10302010178566, "rowwise_fwd": 8.347474038600922, "rowwise_bwd": 7.514089345932007, "global_fwd": 8.263226598501205, "global_bwd": 7.487393915653229, "x_quantize_rowwise": 0.3021806478500366, "g_quantize_rowwise": 1.7319358885288239, "w_quantize_rowwise": 0.15519559383392334, "w_quantize_colwise_transpose": 1.6133114695549011, "w_quantize_global": 0.48247724771499634, "w_quantize_global_transpose": 0.506427139043808, "time_standard": 53.04029583930969, "time_rowwise": 37.3353473842144, "time_global": 36.44480183720589}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 4096, "dim_in": 16384, "wm": 4, "switch": true, "standard_fwd": 17.73649826645851, "standard_gw": 16.359902918338776, "standard_gx": 18.0993489921093, "rowwise_fwd": 7.493957877159119, "rowwise_bwd": 8.352488279342651, "global_fwd": 7.486194372177124, "global_bwd": 8.28903540968895, "x_quantize_rowwise": 1.7313472926616669, "g_quantize_rowwise": 0.30205026268959045, "w_quantize_rowwise": 0.2255477011203766, "w_quantize_colwise_transpose": 2.4363920092582703, "w_quantize_global": 0.4815347492694855, "w_quantize_global_transpose": 0.5103759467601776, "time_standard": 52.195750176906586, "time_rowwise": 36.90168634057045, "time_global": 35.16044095158577}
|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 16384, "dim_in": 4096, "wm": 4, "switch": false, "standard_fwd": 36.309611052274704, "standard_gw": 32.85098075866699, "standard_gx": 34.34552624821663, "rowwise_fwd": 16.74525812268257, "rowwise_bwd": 15.026237815618515, "global_fwd": 16.574162989854813, "global_bwd": 14.977734535932541, "x_quantize_rowwise": 0.5954466760158539, "g_quantize_rowwise": 3.4569576382637024, "w_quantize_rowwise": 0.15521422028541565, "w_quantize_colwise_transpose": 1.6133897006511688, "w_quantize_global": 0.4822872579097748, "w_quantize_global_transpose": 0.5065612494945526, "time_standard": 103.50611805915833, "time_rowwise": 70.44348493218422, "time_global": 69.44413110613823}
|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 4096, "dim_in": 16384, "wm": 4, "switch": true, "standard_fwd": 35.40017828345299, "standard_gw": 33.037226647138596, "standard_gx": 36.30436211824417, "rowwise_fwd": 15.043705701828003, "rowwise_bwd": 16.756191849708557, "global_fwd": 15.011314302682877, "global_bwd": 16.580048948526382, "x_quantize_rowwise": 3.4548528492450714, "g_quantize_rowwise": 0.5951337516307831, "w_quantize_rowwise": 0.22584572434425354, "w_quantize_colwise_transpose": 2.4329908192157745, "w_quantize_global": 0.4813261330127716, "w_quantize_global_transpose": 0.5101598799228668, "time_standard": 104.74176704883575, "time_rowwise": 71.54594734311104, "time_global": 69.67006251215935}
|
||||
{"repeat": 64, "batch_size": 131072, "dim_out": 16384, "dim_in": 4096, "wm": 4, "switch": false, "standard_fwd": 73.40333238244057, "standard_gw": 73.76311346888542, "standard_gx": 70.41774317622185, "rowwise_fwd": 33.37597846984863, "rowwise_bwd": 30.345775187015533, "global_fwd": 33.00366923213005, "global_bwd": 30.218638479709625, "x_quantize_rowwise": 1.1825822293758392, "g_quantize_rowwise": 6.902601569890976, "w_quantize_rowwise": 0.15529245138168335, "w_quantize_colwise_transpose": 1.6109198331832886, "w_quantize_global": 0.48149004578590393, "w_quantize_global_transpose": 0.5066059529781342, "time_standard": 217.58418902754784, "time_rowwise": 147.33626320958138, "time_global": 146.05870097875595}
|
||||
{"repeat": 64, "batch_size": 131072, "dim_out": 4096, "dim_in": 16384, "wm": 4, "switch": true, "standard_fwd": 71.5160183608532, "standard_gw": 73.76786693930626, "standard_gx": 72.98104092478752, "rowwise_fwd": 30.291248112916946, "rowwise_bwd": 33.36654230952263, "global_fwd": 30.181586742401123, "global_bwd": 33.082425594329834, "x_quantize_rowwise": 6.902430206537247, "g_quantize_rowwise": 1.1815279722213745, "w_quantize_rowwise": 0.2262219786643982, "w_quantize_colwise_transpose": 2.4421699345111847, "w_quantize_global": 0.4816502332687378, "w_quantize_global_transpose": 0.5105249583721161, "time_standard": 218.26492622494698, "time_rowwise": 148.17800745368004, "time_global": 146.1080126464367}
|
||||
|
|
@ -12,12 +12,18 @@ if __name__ == '__main__':
|
|||
fig = plt.figure(tight_layout=True, figsize=(12,3.5))
|
||||
gs = gridspec.GridSpec(1, 2)
|
||||
|
||||
dims_to_consider = [1024, 1280, 1408, 1664, 2048, 4096]
|
||||
batch_size_for_plot1 = 32768
|
||||
batch_sizes_for_plot2 = [2**14, 2**15, 2**16, 2**17]
|
||||
dims_to_xtick = [1024, 2048, 4096]
|
||||
logscale_plot1 = True
|
||||
|
||||
ax = fig.add_subplot(gs[0, 0])
|
||||
|
||||
rdf = pd.read_json('tests/triton_tests/info.jsonl', lines=True)
|
||||
df = rdf[rdf.batch_size == 32768]
|
||||
rdf = pd.read_json('speed_benchmark/info_a100_py2.jsonl', lines=True)
|
||||
df = rdf[rdf.batch_size == batch_size_for_plot1]
|
||||
|
||||
# first plot the time occupied by different operations
|
||||
for k, marker, ls, color, name in [
|
||||
('standard_gx+standard_gw+standard_fwd', 's', '-', 'C2', 'Standard fp16 (sum of parts)'),
|
||||
('x_quantize_rowwise+g_quantize_rowwise+w_quantize_global+w_quantize_global_transpose+standard_gw+global_fwd+global_bwd', 'o', '-', 'C4', 'SwitchBack int8 (sum of parts)'),
|
||||
|
|
@ -29,17 +35,15 @@ if __name__ == '__main__':
|
|||
('global_fwd', '^', '--', 'C4', 'Int8 Matmul XW (switchback)'),
|
||||
('global_bwd', '^', '-.', 'C4', 'Int8 Matmul GW (switchback)'),
|
||||
|
||||
#### time_global = info['x_quantize_rowwise'] + info['g_quantize_rowwise'] + info['w_quantize_global'] + info['w_quantize_global_transpose'] + info['standard_gw'] + info['global_fwd'] + info['global_bwd']
|
||||
|
||||
('x_quantize_rowwise', 'P', '--', 'C4', 'Quantize rowwise X (switchback)'),
|
||||
('g_quantize_rowwise', 'P', '-.', 'C4', 'Quantize rowwise G (switchback)'),
|
||||
('w_quantize_global', '.', '--', 'C4', 'Quatnize global W (switchback)'),
|
||||
('w_quantize_global_transpose', '.', '-.', 'C4', 'Quantize gloabl and\ntranspose W (switchback)'),
|
||||
#('standard_gw', '.', '--', 'C1', 'standard_gw'),
|
||||
]:
|
||||
xs = []
|
||||
ys = []
|
||||
for embed_dim in [1024, 1280, 1408, 1664, 2048, 4096]:
|
||||
for embed_dim in dims_to_consider:
|
||||
# average over dim -> 4*dim and 4*dim -> dim
|
||||
df_ = df[df.dim_in == embed_dim]
|
||||
df_ = df_[df_.dim_out == embed_dim * 4]
|
||||
xs.append(embed_dim)
|
||||
|
|
@ -56,24 +60,20 @@ if __name__ == '__main__':
|
|||
ax.plot(xs, ys, color=color, label=name, marker=marker, markersize=5 if marker=='s' else 5, linestyle=ls, linewidth=2 if '+' in k else 1.)
|
||||
|
||||
|
||||
|
||||
|
||||
ax.set_xlabel('dim', fontsize=13)
|
||||
ax.set_ylabel('time (ms)', fontsize=13)
|
||||
# make a legend which is below the plot
|
||||
|
||||
|
||||
|
||||
ax.grid()
|
||||
|
||||
ax.set_xscale('log')
|
||||
#ax.set_yscale('log')
|
||||
if logscale_plot1:
|
||||
ax.set_yscale('log')
|
||||
|
||||
ax.tick_params(axis='x', labelsize=11)
|
||||
ax.tick_params(axis='y', labelsize=11)
|
||||
|
||||
ax.set_xticks([1024, 2048, 4096])
|
||||
ax.set_xticklabels([1024, 2048, 4096])
|
||||
ax.set_xticks(dims_to_xtick)
|
||||
ax.set_xticklabels(dims_to_xtick)
|
||||
ax.set_xticks([], minor=True)
|
||||
|
||||
leg = ax.legend(loc='upper center', bbox_to_anchor=(-0.64, 1.), ncol=1, fontsize=10)
|
||||
|
|
@ -86,7 +86,7 @@ if __name__ == '__main__':
|
|||
ax = fig.add_subplot(gs[0, 1])
|
||||
|
||||
# now plot the % speedup for different batch sizes
|
||||
for j, batch_size in enumerate([2**14, 2**15, 2**16, 2**17]):
|
||||
for j, batch_size in enumerate(batch_sizes_for_plot2):
|
||||
all_xs, all_ys = [], []
|
||||
for k, marker, ls, color, name in [
|
||||
('standard_gx+standard_gw+standard_fwd', 's', '-', 'C2', 'Standard fp16 (total time)'),
|
||||
|
|
@ -95,7 +95,7 @@ if __name__ == '__main__':
|
|||
|
||||
xs, ys = [], []
|
||||
df = rdf[rdf.batch_size == batch_size]
|
||||
for embed_dim in [1024, 1280, 1408, 1664, 2048, 4096]:
|
||||
for embed_dim in dims_to_consider:
|
||||
df_ = df[df.dim_in == embed_dim]
|
||||
df_ = df_[df_.dim_out == embed_dim * 4]
|
||||
xs.append(embed_dim)
|
||||
|
|
@ -125,13 +125,13 @@ if __name__ == '__main__':
|
|||
ax.tick_params(axis='x', labelsize=11)
|
||||
ax.tick_params(axis='y', labelsize=11)
|
||||
|
||||
ax.set_xticks([1024, 2048, 4096])
|
||||
ax.set_xticklabels([1024, 2048, 4096])
|
||||
ax.set_xticks(dims_to_xtick)
|
||||
ax.set_xticklabels(dims_to_xtick)
|
||||
ax.set_xticks([], minor=True)
|
||||
|
||||
ax.set_title(' Linear layer summary, varying dimensions', fontsize=10, loc='left', y=1.05, pad=-20)
|
||||
|
||||
|
||||
|
||||
plt.savefig('tests/triton_tests/plot1.pdf', bbox_inches='tight')
|
||||
plt.savefig('speed_benchmark/plot_with_info.pdf', bbox_inches='tight')
|
||||
|
||||
Binary file not shown.
101
speed_benchmark/speed_benchmark.py
Normal file
101
speed_benchmark/speed_benchmark.py
Normal file
|
|
@ -0,0 +1,101 @@
|
|||
import json
|
||||
|
||||
import time
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from bitsandbytes.nn.triton_utils.v0.quantize_rowwise import quantize_rowwise
|
||||
from bitsandbytes.nn.triton_utils.v0.quantize_columnwise_and_transpose import quantize_columnwise_and_transpose
|
||||
from bitsandbytes.nn.triton_utils.v0.int8_matmul_rowwise_dequantize import int8_matmul_rowwise_dequantize
|
||||
from bitsandbytes.nn.triton_utils.v0.quantize_global import quantize_global, quantize_global_transpose
|
||||
from bitsandbytes.nn.triton_utils.v0.int8_matmul_mixed_dequanitze import int8_matmul_mixed_dequanitze
|
||||
|
||||
# KNOW ISSUE: need to optimize "w_quantize_colwise_transpose" when embeddim is too large.
|
||||
|
||||
def get_time(k, fn, info_dict):
|
||||
|
||||
for _ in range(repeat // 2):
|
||||
fn()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
fn()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info_dict[k] = ms
|
||||
|
||||
if __name__ == '__main__':
|
||||
torch.manual_seed(0)
|
||||
wm = 4
|
||||
for dim in [1024, 1280, 1408, 1664, 2048, 4096]:
|
||||
# note "batch_size" is actually "batch_size * embed_dim", which is why it's large
|
||||
for batch_size in [256*32, 256*64, 256*128, 256*256, 256*512]:
|
||||
|
||||
# switch switches dim_in and dim_out
|
||||
for switch in [False, True]:
|
||||
|
||||
# hparams
|
||||
repeat = 64
|
||||
batch_size = batch_size
|
||||
dim_out = dim * wm
|
||||
dim_in = dim
|
||||
if switch:
|
||||
dim_out = dim
|
||||
dim_in = wm * dim
|
||||
|
||||
dim_in = round(dim_in)
|
||||
dim_out = round(dim_out)
|
||||
|
||||
# simulate forward pass
|
||||
x = torch.randn(batch_size, dim_in, dtype=torch.float16).cuda()
|
||||
g = torch.randn(batch_size, dim_out, dtype=torch.float16).cuda()
|
||||
w = torch.randn(dim_out, dim_in, dtype=torch.float16).cuda()
|
||||
|
||||
x_int8 = x.clone().to(torch.int8)
|
||||
g_int8 = g.clone().to(torch.int8)
|
||||
w_int8 = w.clone().to(torch.int8)
|
||||
wt_int8 = w.t().contiguous().clone().to(torch.int8)
|
||||
state_x_rowwise = x.max(dim=1)[0]
|
||||
state_g_rowwise = g.max(dim=1)[0]
|
||||
state_w_columnwise = w.max(dim=0)[0]
|
||||
state_w_rowwise = w.max(dim=1)[0]
|
||||
state_w_global = w.max()
|
||||
|
||||
info = {'repeat' : repeat, 'batch_size' : batch_size, 'dim_out' : dim_out, 'dim_in' : dim_in, 'wm' : wm, 'switch' : switch}
|
||||
|
||||
get_time('standard_fwd', lambda : x.matmul(w.t()), info)
|
||||
get_time('standard_gw', lambda : g.t().matmul(x), info)
|
||||
get_time('standard_gx', lambda : g.matmul(w), info)
|
||||
get_time('rowwise_fwd', lambda : int8_matmul_rowwise_dequantize(x_int8, w_int8.t(), state_x_rowwise, state_w_columnwise, None), info)
|
||||
get_time('rowwise_bwd', lambda : int8_matmul_rowwise_dequantize(g_int8, wt_int8.t(), state_x_rowwise, state_w_rowwise, None), info)
|
||||
get_time('global_fwd', lambda : int8_matmul_mixed_dequanitze(x_int8, w_int8.t(), state_x_rowwise, state_w_global, None), info)
|
||||
get_time('global_bwd', lambda : int8_matmul_mixed_dequanitze(g_int8, wt_int8.t(), state_x_rowwise, state_w_global, None), info)
|
||||
get_time('x_quantize_rowwise', lambda : quantize_rowwise(x), info)
|
||||
get_time('g_quantize_rowwise', lambda : quantize_rowwise(g), info)
|
||||
get_time('w_quantize_rowwise', lambda : quantize_rowwise(w), info)
|
||||
get_time('w_quantize_colwise_transpose', lambda : quantize_columnwise_and_transpose(w), info)
|
||||
get_time('w_quantize_global', lambda : quantize_global(w), info)
|
||||
get_time('w_quantize_global_transpose', lambda : quantize_global_transpose(w), info)
|
||||
|
||||
time_standard = info['standard_fwd'] + info['standard_gx'] + info['standard_gw']
|
||||
time_rowwise = info['x_quantize_rowwise'] + info['g_quantize_rowwise'] + info['w_quantize_colwise_transpose'] + info['w_quantize_rowwise'] + info['standard_gw'] + info['rowwise_fwd'] + info['rowwise_bwd']
|
||||
time_global = info['x_quantize_rowwise'] + info['g_quantize_rowwise'] + info['w_quantize_global'] + info['w_quantize_global_transpose'] + info['standard_gw'] + info['global_fwd'] + info['global_bwd']
|
||||
|
||||
print('TOTAL STANDARD', time_standard)
|
||||
print('TOTAL ROWWISE', time_rowwise)
|
||||
print('TOTAL GLOBAL', time_global)
|
||||
|
||||
print('speedup', -100*(time_global - time_standard)/time_standard)
|
||||
|
||||
info['time_standard'] = time_standard
|
||||
info['time_rowwise'] = time_rowwise
|
||||
info['time_global'] = time_global
|
||||
|
||||
info_json = json.dumps(info)
|
||||
|
||||
with open("speed_benchmark/info_a100_py2.jsonl", "a") as file:
|
||||
file.write(info_json + "\n")
|
||||
|
|
@ -1,44 +1,57 @@
|
|||
import pytest
|
||||
import torch
|
||||
|
||||
from bitsandbytes.nn.triton_based_modules import SwitchBackLinear, SwitchBackGlobalLinear
|
||||
from bitsandbytes.nn.triton_based_modules import SwitchBackLinear
|
||||
from bitsandbytes.nn import Linear8bitLt
|
||||
|
||||
|
||||
|
||||
@pytest.mark.parametrize("triton_module", [SwitchBackGlobalLinear, SwitchBackLinear])
|
||||
def test_switchbatch(triton_module):
|
||||
@pytest.mark.parametrize("vectorrize", [False, True])
|
||||
def test_switchback(vectorrize):
|
||||
for dim in [83, 17, 128]:
|
||||
for batch in [13, 128, 256]:
|
||||
|
||||
standard = torch.nn.Linear(dim, 4 * dim).cuda().half()
|
||||
switchback = triton_module(dim, 4 * dim).cuda().half()
|
||||
print('vectorrize', vectorrize)
|
||||
switchback = SwitchBackLinear(dim, 4 * dim, vectorize=vectorrize).cuda().half()
|
||||
baseline = Linear8bitLt(dim, 4 * dim).cuda().half()
|
||||
switchback.weight.data.copy_(standard.weight)
|
||||
switchback.bias.data.copy_(standard.bias)
|
||||
baseline.weight.data.copy_(standard.weight)
|
||||
baseline.bias.data.copy_(standard.bias)
|
||||
|
||||
x1 = torch.randn(batch, dim).cuda().half().requires_grad_(True)
|
||||
x2 = x1.clone().detach().requires_grad_(True)
|
||||
x3 = x1.clone().detach().requires_grad_(True)
|
||||
|
||||
for i in range(100):
|
||||
x1 = torch.randn(batch, dim).cuda().half().requires_grad_(True)
|
||||
x2 = x1.clone().detach().requires_grad_(True)
|
||||
print('standard')
|
||||
out_standard = standard(x1)
|
||||
print('switchback')
|
||||
out_sb = switchback(x1)
|
||||
out_standard = standard(x1)
|
||||
(2**10 * out_standard.abs().mean()).backward()
|
||||
|
||||
(out_standard.abs().mean()).backward()
|
||||
(out_sb.abs().mean()).backward()
|
||||
out_sb = switchback(x2)
|
||||
(2**10 * out_sb.abs().mean()).backward()
|
||||
|
||||
err_sb = (out_standard - out_sb).abs().mean()
|
||||
print('OUT', err_sb)
|
||||
out_baseline = baseline(x3)
|
||||
(2**10 * out_baseline.abs().mean()).backward()
|
||||
|
||||
err_sb = (standard.bias.grad - switchback.bias.grad).abs().mean()
|
||||
err_sb = (out_standard - out_sb).abs().mean()
|
||||
err_baseline = (out_standard - out_baseline).abs().mean()
|
||||
print('OUT', err_sb, err_baseline)
|
||||
assert err_sb < 2 * err_baseline
|
||||
|
||||
print('GW2', err_sb)
|
||||
err_sb = (standard.bias.grad - switchback.bias.grad).abs().mean()
|
||||
err_baseline = (standard.bias.grad - baseline.bias.grad).abs().mean()
|
||||
|
||||
err_sb = (standard.weight.grad - switchback.weight.grad).abs().mean()
|
||||
print('GW2', err_sb, err_baseline)
|
||||
assert err_sb < 2 * err_baseline
|
||||
|
||||
print('GW1', err_sb)
|
||||
err_sb = (standard.weight.grad - switchback.weight.grad).abs().mean()
|
||||
err_baseline = (standard.weight.grad - baseline.weight.grad).abs().mean()
|
||||
|
||||
#err_sb = (x1.grad - x2.grad).abs().mean()
|
||||
print('GW1', err_sb, err_baseline)
|
||||
assert err_sb < 2 * err_baseline
|
||||
|
||||
#print('GX1', err_sb)
|
||||
err_sb = (x1.grad - x2.grad).abs().mean()
|
||||
err_baseline = (x1.grad - x3.grad).abs().mean()
|
||||
|
||||
print('GX1', err_sb, err_baseline)
|
||||
assert err_sb < 2 * err_baseline
|
||||
|
||||
|
|
|
|||
|
|
@ -1,363 +0,0 @@
|
|||
|
||||
import torch
|
||||
import json
|
||||
from bitsandbytes.nn.triton_based_modules import SwitchBackGlobalMLP, SwitchBackGlobalLinear, StandardLinear
|
||||
import time
|
||||
|
||||
# class AttentionOld(torch.nn.Module):
|
||||
# def __init__(
|
||||
# self,
|
||||
# dim,
|
||||
# num_heads=8,
|
||||
# qkv_bias=True,
|
||||
# scaled_cosine=False,
|
||||
# scale_heads=False,
|
||||
# attn_drop=0.,
|
||||
# proj_drop=0.,
|
||||
# linear_module=torch.nn.Linear,
|
||||
# ):
|
||||
# super().__init__()
|
||||
# self.scaled_cosine = scaled_cosine
|
||||
# self.scale_heads = scale_heads
|
||||
# assert dim % num_heads == 0, 'dim should be divisible by num_heads'
|
||||
# self.num_heads = num_heads
|
||||
# self.head_dim = dim // num_heads
|
||||
# self.scale = self.head_dim ** -0.5
|
||||
|
||||
# self.in_proj_linear = linear_module(dim, 3 * dim, bias = qkv_bias)
|
||||
|
||||
# self.attn_drop = torch.nn.Dropout(attn_drop)
|
||||
# if self.scale_heads:
|
||||
# self.head_scale = torch.nn.Parameter(torch.ones((num_heads, 1, 1)))
|
||||
# else:
|
||||
# self.head_scale = None
|
||||
# self.out_proj = linear_module(dim, dim)
|
||||
# self.out_drop = torch.nn.Dropout(proj_drop)
|
||||
|
||||
# def forward(self, x, attn_mask = None):
|
||||
# L, N, C = x.shape
|
||||
|
||||
# q, k, v = self.in_proj_linear(x).chunk(3, dim=-1)
|
||||
|
||||
# q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
|
||||
# k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
|
||||
# v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1)
|
||||
|
||||
# q = q * self.scale
|
||||
# attn = torch.bmm(q, k.transpose(-1, -2))
|
||||
|
||||
# if attn_mask is not None:
|
||||
# if attn_mask.dtype == torch.bool:
|
||||
# new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
|
||||
# new_attn_mask.masked_fill_(attn_mask, float("-inf"))
|
||||
# attn_mask = new_attn_mask
|
||||
# attn += attn_mask
|
||||
|
||||
# attn = attn.softmax(dim=-1)
|
||||
# attn = self.attn_drop(attn)
|
||||
|
||||
# x = torch.bmm(attn, v)
|
||||
# x = x.transpose(0, 1).reshape(L, N, C)
|
||||
|
||||
# x = self.out_proj(x)
|
||||
# x = self.out_drop(x)
|
||||
# return x
|
||||
|
||||
class Attention(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim,
|
||||
num_heads=8,
|
||||
qkv_bias=True,
|
||||
scaled_cosine=False,
|
||||
scale_heads=False,
|
||||
attn_drop=0.,
|
||||
proj_drop=0.,
|
||||
linear_module=torch.nn.Linear,
|
||||
):
|
||||
super().__init__()
|
||||
self.scaled_cosine = scaled_cosine
|
||||
self.scale_heads = scale_heads
|
||||
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = dim // num_heads
|
||||
self.scale = self.head_dim ** -0.5
|
||||
|
||||
self.ln = torch.nn.LayerNorm(dim)
|
||||
|
||||
self.in_proj_linear = linear_module(dim, 3 * dim, bias = qkv_bias)
|
||||
|
||||
self.attn_drop = torch.nn.Dropout(attn_drop)
|
||||
if self.scale_heads:
|
||||
self.head_scale = torch.nn.Parameter(torch.ones((num_heads, 1, 1)))
|
||||
else:
|
||||
self.head_scale = None
|
||||
self.out_proj = linear_module(dim, dim)
|
||||
self.out_drop = torch.nn.Dropout(proj_drop)
|
||||
|
||||
def forward(self, x, attn_mask = None):
|
||||
q, k, v = self.in_proj_linear(self.ln(x)).chunk(3, dim=-1)
|
||||
x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask)
|
||||
x = self.out_proj(x)
|
||||
return x
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
|
||||
for dim in [1024, 1280, 1408, 1664, 2048]:
|
||||
for batch in [2**14, 2**15, 2**16, 2**17]:
|
||||
|
||||
# if dim != 4096 or batch != 2**17:
|
||||
# continue
|
||||
|
||||
x1 = torch.randn( batch // 256, 256, dim ).cuda().requires_grad_(True)
|
||||
qu = torch.randn( batch // 256, 256, dim ).cuda().requires_grad_(True)
|
||||
ke = torch.randn( batch // 256, 256, dim ).cuda().requires_grad_(True)
|
||||
va = torch.randn( batch // 256, 256, dim ).cuda().requires_grad_(True)
|
||||
|
||||
standard = Attention(dim).cuda()
|
||||
my_standard = Attention(dim, linear_module=StandardLinear).cuda()
|
||||
sb = Attention(dim, linear_module=SwitchBackGlobalLinear).cuda()
|
||||
standard_compiled = torch.compile(standard)
|
||||
ln_model = torch.nn.Sequential(
|
||||
torch.nn.LayerNorm(dim),
|
||||
torch.nn.LayerNorm(dim),
|
||||
).cuda()
|
||||
ln_model_compiled = torch.compile(
|
||||
ln_model
|
||||
)
|
||||
gelu_model = torch.nn.Sequential(
|
||||
torch.nn.GELU(),
|
||||
).cuda()
|
||||
gelu_model_compiled = torch.compile(
|
||||
gelu_model
|
||||
)
|
||||
|
||||
|
||||
print('Model part 2')
|
||||
|
||||
repeat = 32
|
||||
|
||||
info = {'repeat' : repeat, 'batch_size' : batch, 'dim' : dim}
|
||||
|
||||
|
||||
k = 'attn'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_attn = torch.nn.functional.scaled_dot_product_attention(qu, ke, va)
|
||||
((2 ** 16) * out_attn).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_attn = torch.nn.functional.scaled_dot_product_attention(qu, ke, va)
|
||||
((2 ** 16) * out_attn).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
k = 'ln'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out = ln_model(x1)
|
||||
((2 ** 16) * out).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out = ln_model(x1)
|
||||
((2 ** 16) * out).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
x1.grad.zero_()
|
||||
|
||||
k = 'ln_compiled'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out = ln_model_compiled(x1)
|
||||
((2 ** 16) * out).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out = ln_model_compiled(x1)
|
||||
((2 ** 16) * out).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
k = 'gelu'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out = gelu_model(x1)
|
||||
((2 ** 16) * out).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out = gelu_model(x1)
|
||||
((2 ** 16) * out).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
x1.grad.zero_()
|
||||
|
||||
k = 'gelu_compiled'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out = gelu_model_compiled(x1)
|
||||
((2 ** 16) * out).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out = gelu_model_compiled(x1)
|
||||
((2 ** 16) * out).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
x1.grad.zero_()
|
||||
|
||||
k = 'standard'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_standard = standard(x1)
|
||||
((2 ** 16) * out_standard).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_standard = standard(x1)
|
||||
((2 ** 16) * out_standard).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
x1.grad.zero_()
|
||||
|
||||
k = 'my_standard'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_my_standard = my_standard(x1)
|
||||
((2 ** 16) * out_my_standard).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_my_standard = my_standard(x1)
|
||||
((2 ** 16) * out_my_standard).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
#
|
||||
#
|
||||
|
||||
x1.grad.zero_()
|
||||
|
||||
|
||||
k = 'standard_compiled'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_standard_compiled = standard_compiled(x1)
|
||||
((2 ** 16) * out_standard_compiled).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_standard_compiled = standard_compiled(x1)
|
||||
((2 ** 16) * out_standard_compiled).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
x1.grad.zero_()
|
||||
|
||||
|
||||
k = 'sb'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_sb = sb(x1)
|
||||
((2 ** 16) * out_sb).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_sb = sb(x1)
|
||||
((2 ** 16) * out_sb).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
info_json = json.dumps(info)
|
||||
|
||||
|
||||
with open("tests/triton_tests/attn_info_ln.jsonl", "a") as file:
|
||||
file.write(info_json + "\n")
|
||||
|
||||
|
||||
#exit()
|
||||
|
||||
# err_fused = (out_standard - out_fused).abs().mean()
|
||||
# err_sb = (out_standard - out_sb).abs().mean()
|
||||
# print('OUT', err_fused, err_sb)
|
||||
|
||||
# err_fused = (standard[d].weight.grad - fused_mlp.linear2.weight.grad).abs().mean()
|
||||
# err_sb = (standard[d].weight.grad - sb[d].weight.grad).abs().mean()
|
||||
|
||||
# print('GW2', err_fused, err_sb)
|
||||
|
||||
# err_fused = (standard[0].weight.grad - fused_mlp.linear1.weight.grad).abs().mean()
|
||||
# err_sb = (standard[0].weight.grad - sb[0].weight.grad).abs().mean()
|
||||
|
||||
# print('GW1', err_fused, err_sb)
|
||||
|
||||
# err_fused = (x1.grad - x2.grad).abs().mean()
|
||||
# err_sb = (x1.grad - x3.grad).abs().mean()
|
||||
|
||||
# print('GX1', err_fused, err_sb)
|
||||
|
||||
# import pdb; pdb.set_trace()
|
||||
|
||||
|
||||
# # NO GELU, ST GRADIENTS, EVERYTHING FINE.
|
||||
|
|
@ -1,20 +0,0 @@
|
|||
{"repeat": 32, "batch_size": 16384, "dim": 1024, "attn": 2.1414458751678467, "ln": 1.6365647315979004, "ln_compiled": 1.799367368221283, "gelu": 1.0930374264717102, "gelu_compiled": 1.094818115234375, "standard": 4.159651696681976, "my_standard": 4.696495831012726, "standard_compiled": 3.675594925880432, "sb": 4.1465312242507935}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1024, "attn": 4.100345075130463, "ln": 3.1594187021255493, "ln_compiled": 3.437422215938568, "gelu": 2.109348773956299, "gelu_compiled": 2.11450457572937, "standard": 7.706902921199799, "my_standard": 8.799396455287933, "standard_compiled": 6.735652685165405, "sb": 7.66376405954361}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1024, "attn": 7.953710854053497, "ln": 6.236426532268524, "ln_compiled": 6.746955215930939, "gelu": 4.164382815361023, "gelu_compiled": 4.171714186668396, "standard": 14.894917607307434, "my_standard": 17.042435705661774, "standard_compiled": 12.985721230506897, "sb": 14.6140456199646}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1024, "attn": 15.638880431652069, "ln": 12.333884835243225, "ln_compiled": 13.272866606712341, "gelu": 8.228793740272522, "gelu_compiled": 8.243747055530548, "standard": 29.425136744976044, "my_standard": 35.08377820253372, "standard_compiled": 25.69487690925598, "sb": 28.760001063346863}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1280, "attn": 2.627238631248474, "ln": 2.0098239183425903, "ln_compiled": 2.4197474122047424, "gelu": 1.3455823063850403, "gelu_compiled": 1.35069340467453, "standard": 5.554787814617157, "my_standard": 6.2290579080581665, "standard_compiled": 5.132324993610382, "sb": 5.4178386926651}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1280, "attn": 5.0596073269844055, "ln": 3.903590142726898, "ln_compiled": 4.719957709312439, "gelu": 2.6203468441963196, "gelu_compiled": 2.627365291118622, "standard": 10.546617209911346, "my_standard": 11.850126087665558, "standard_compiled": 9.685918688774109, "sb": 10.088451206684113}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1280, "attn": 9.845800697803497, "ln": 7.711298763751984, "ln_compiled": 9.292080998420715, "gelu": 5.172915756702423, "gelu_compiled": 5.180932581424713, "standard": 21.371990442276, "my_standard": 23.921720683574677, "standard_compiled": 19.669152796268463, "sb": 20.267993211746216}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1280, "attn": 19.375711679458618, "ln": 15.333592891693115, "ln_compiled": 18.245264887809753, "gelu": 10.264746844768524, "gelu_compiled": 10.283775627613068, "standard": 41.79700464010239, "my_standard": 45.84744572639465, "standard_compiled": 38.35208714008331, "sb": 38.35364431142807}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1408, "attn": 2.9110386967658997, "ln": 2.1998360753059387, "ln_compiled": 2.581551671028137, "gelu": 1.4731436967849731, "gelu_compiled": 1.478634774684906, "standard": 6.764143705368042, "my_standard": 7.331632077693939, "standard_compiled": 6.24605268239975, "sb": 6.325609982013702}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1408, "attn": 5.542516708374023, "ln": 4.289716482162476, "ln_compiled": 5.065307021141052, "gelu": 2.8742849826812744, "gelu_compiled": 2.882353961467743, "standard": 12.749537825584412, "my_standard": 13.79828155040741, "standard_compiled": 11.728867888450623, "sb": 11.642806231975555}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1408, "attn": 10.80312579870224, "ln": 8.471302688121796, "ln_compiled": 9.96796041727066, "gelu": 5.681410431861877, "gelu_compiled": 5.6905597448349, "standard": 25.19702911376953, "my_standard": 27.226239442825317, "standard_compiled": 23.22910726070404, "sb": 22.682294249534607}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1408, "attn": 21.284908056259155, "ln": 16.85701310634613, "ln_compiled": 19.643358886241913, "gelu": 11.292420327663422, "gelu_compiled": 11.314474046230316, "standard": 50.06787180900574, "my_standard": 54.29378151893616, "standard_compiled": 44.58653926849365, "sb": 45.359253883361816}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1664, "attn": 3.382459282875061, "ln": 2.6206374168395996, "ln_compiled": 2.9666870832443237, "gelu": 1.7263293266296387, "gelu_compiled": 1.7317384481430054, "standard": 8.414775133132935, "my_standard": 9.117811918258667, "standard_compiled": 7.7542513608932495, "sb": 7.70898163318634}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1664, "attn": 6.468378007411957, "ln": 5.125559866428375, "ln_compiled": 5.791269242763519, "gelu": 3.3864825963974, "gelu_compiled": 3.3920034766197205, "standard": 16.016244888305664, "my_standard": 17.25083589553833, "standard_compiled": 14.60808515548706, "sb": 14.347739517688751}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1664, "attn": 12.645229697227478, "ln": 10.13532280921936, "ln_compiled": 11.427387595176697, "gelu": 6.6957250237464905, "gelu_compiled": 6.711684167385101, "standard": 31.792201101779938, "my_standard": 34.31189805269241, "standard_compiled": 29.10037338733673, "sb": 28.3128023147583}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1664, "attn": 24.970605969429016, "ln": 20.182937383651733, "ln_compiled": 22.7489173412323, "gelu": 13.326868414878845, "gelu_compiled": 13.345755636692047, "standard": 63.46555054187775, "my_standard": 70.19880414009094, "standard_compiled": 56.40875548124313, "sb": 56.22846633195877}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 2048, "attn": 4.080049693584442, "ln": 3.2655522227287292, "ln_compiled": 3.3329352736473083, "gelu": 2.108432352542877, "gelu_compiled": 2.114713191986084, "standard": 11.370822787284851, "my_standard": 12.234866619110107, "standard_compiled": 10.377615690231323, "sb": 10.209612548351288}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 2048, "attn": 7.74645060300827, "ln": 6.418220698833466, "ln_compiled": 6.55733048915863, "gelu": 4.163652658462524, "gelu_compiled": 4.171028733253479, "standard": 21.39316499233246, "my_standard": 23.04024249315262, "standard_compiled": 19.431106746196747, "sb": 18.732361495494843}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 2048, "attn": 15.235155820846558, "ln": 12.684382498264313, "ln_compiled": 12.895286083221436, "gelu": 8.228868246078491, "gelu_compiled": 8.242718875408173, "standard": 42.55136102437973, "my_standard": 45.82635313272476, "standard_compiled": 38.663335144519806, "sb": 36.76284849643707}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 2048, "attn": 30.24454414844513, "ln": 25.25731921195984, "ln_compiled": 25.67601203918457, "gelu": 16.384944319725037, "gelu_compiled": 16.409948468208313, "standard": 84.26841348409653, "my_standard": 91.10662341117859, "standard_compiled": 76.89539343118668, "sb": 71.73164188861847}
|
||||
|
|
@ -1,353 +0,0 @@
|
|||
import json
|
||||
|
||||
import time
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import bitsandbytes.nn as bnn
|
||||
from bitsandbytes.nn.triton_based_modules import SwitchBackLinear, SwitchBackGlobalLinear, StandardLinear
|
||||
|
||||
from bitsandbytes.nn.triton_utils.v0.quantize_rowwise_nogroup import quantize_rowwise_nogroup
|
||||
from bitsandbytes.nn.triton_utils.v0.quantize_columnwise_nogroup_transpose import quantize_columnwise_nogroup_transpose
|
||||
from bitsandbytes.nn.triton_utils.v0.int8_matmul_rowwise_dequantize_bias import int8_matmul_rowwise_dequantize_bias
|
||||
from bitsandbytes.nn.triton_utils.v0.int8_matmul_rowwise_dequantize import int8_matmul_rowwise_dequantize
|
||||
from bitsandbytes.nn.triton_utils.v0.quantize_global import quantize_global, quantize_global_transpose
|
||||
from bitsandbytes.nn.triton_utils.v0.int8_matmul_mixed_dequanitze import int8_matmul_mixed_dequanitze, int8_matmul_mixed_dequanitze_bias
|
||||
|
||||
# KNOW ISSUE: need to optimize "w_quantize_colwise_transpose" when embeddim is too large.
|
||||
# not that big of an issue.
|
||||
|
||||
def get_time_standard_fwd(k, v):
|
||||
|
||||
x = torch.randn(batch_size, dim_in, dtype=torch.float16).cuda()
|
||||
g = torch.randn(batch_size, dim_out, dtype=torch.float16).cuda()
|
||||
|
||||
##### time matmul 1
|
||||
for _ in range(repeat // 2):
|
||||
g.t().matmul(x)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
g.t().matmul(x)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
print(f"time {k}: {(end - start) / repeat * 1000:.3f} ms")
|
||||
return (end - start) / repeat * 1000
|
||||
|
||||
if __name__ == '__main__':
|
||||
torch.manual_seed(0)
|
||||
#for (dim, wm) in [(1024, 4), (1280, 4), (1408, 4.3637), (1664, 4.9231), (2048, 4), (4096, 4), (8096, 4)]
|
||||
for (dim, wm) in [(1408, 4), (1664, 4),]:
|
||||
|
||||
for batch_size in [256*32, 256*64, 256*128, 256*256, 256*512]:
|
||||
#for batch_size in [256*256, 256*512]:
|
||||
|
||||
for switch in [False, True]:
|
||||
|
||||
|
||||
# hparams
|
||||
repeat = 64
|
||||
batch_size = batch_size
|
||||
dim_out = dim * wm
|
||||
dim_in = dim
|
||||
if switch:
|
||||
dim_out = dim
|
||||
dim_in = wm * dim
|
||||
|
||||
dim_in = round(dim_in)
|
||||
dim_out = round(dim_out)
|
||||
|
||||
|
||||
# simulate forward pass
|
||||
x = torch.randn(batch_size, dim_in, dtype=torch.float16).cuda()
|
||||
g = torch.randn(batch_size, dim_out, dtype=torch.float16).cuda()
|
||||
w = torch.randn(dim_out, dim_in, dtype=torch.float16).cuda()
|
||||
|
||||
x_int8 = x.clone().to(torch.int8)
|
||||
g_int8 = g.clone().to(torch.int8)
|
||||
w_int8 = w.clone().to(torch.int8)
|
||||
wt_int8 = w.t().contiguous().clone().to(torch.int8)
|
||||
state_x_rowwise = x.max(dim=1)[0]
|
||||
state_g_rowwise = g.max(dim=1)[0]
|
||||
state_w_columnwise = w.max(dim=0)[0]
|
||||
state_w_rowwise = w.max(dim=1)[0]
|
||||
state_w_global = w.max()
|
||||
|
||||
info = {'repeat' : repeat, 'batch_size' : batch_size, 'dim_out' : dim_out, 'dim_in' : dim_in, 'wm' : wm, 'switch' : switch}
|
||||
|
||||
k = 'standard_fwd'
|
||||
for _ in range(repeat // 2):
|
||||
x.matmul(w.t())
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
x.matmul(w.t())
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
k = 'standard_gw'
|
||||
for _ in range(repeat // 2):
|
||||
g.t().matmul(x)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
g.t().matmul(x)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
k = 'standard_gx'
|
||||
for _ in range(repeat // 2):
|
||||
g.matmul(w)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
g.matmul(w)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
|
||||
k = 'rowwise_fwd'
|
||||
for _ in range(repeat // 2):
|
||||
int8_matmul_rowwise_dequantize(x_int8, w_int8.t(), state_x_rowwise, state_w_columnwise)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
int8_matmul_rowwise_dequantize(x_int8, w_int8.t(), state_x_rowwise, state_w_columnwise)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
k = 'rowwise_bwd'
|
||||
for _ in range(repeat // 2):
|
||||
int8_matmul_rowwise_dequantize(g_int8, wt_int8.t(), state_x_rowwise, state_w_rowwise)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
int8_matmul_rowwise_dequantize(g_int8, wt_int8.t(), state_x_rowwise, state_w_rowwise)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
k = 'global_fwd'
|
||||
for _ in range(repeat // 2):
|
||||
int8_matmul_mixed_dequanitze(x_int8, w_int8.t(), state_x_rowwise, state_w_global)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
int8_matmul_mixed_dequanitze(x_int8, w_int8.t(), state_x_rowwise, state_w_global)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
k = 'global_bwd'
|
||||
for _ in range(repeat // 2):
|
||||
int8_matmul_mixed_dequanitze(g_int8, wt_int8.t(), state_x_rowwise, state_w_global)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
int8_matmul_mixed_dequanitze(g_int8, wt_int8.t(), state_x_rowwise, state_w_global)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
k = 'x_quantize_rowwise'
|
||||
for _ in range(repeat // 2):
|
||||
quantize_rowwise_nogroup(x)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
quantize_rowwise_nogroup(x)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
k = 'g_quantize_rowwise'
|
||||
for _ in range(repeat // 2):
|
||||
quantize_rowwise_nogroup(g)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
quantize_rowwise_nogroup(g)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
k = 'w_quantize_rowwise'
|
||||
for _ in range(repeat // 2):
|
||||
quantize_rowwise_nogroup(w)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
quantize_rowwise_nogroup(w)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
k = 'w_quantize_colwise_transpose'
|
||||
for _ in range(repeat // 2):
|
||||
quantize_columnwise_nogroup_transpose(w)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
quantize_columnwise_nogroup_transpose(w)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
k = 'w_quantize_global'
|
||||
for _ in range(repeat // 2):
|
||||
quantize_global(w)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
quantize_global(w)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
k = 'w_quantize_global_transpose'
|
||||
for _ in range(repeat // 2):
|
||||
quantize_global_transpose(w)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
quantize_global_transpose(w)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
k = 'cast_x'
|
||||
for _ in range(repeat // 2):
|
||||
newx = x.to(torch.int8)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
newx = x.to(torch.int8)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
|
||||
k = 'cast_g'
|
||||
for _ in range(repeat // 2):
|
||||
newx = g.to(torch.int8)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
newx = g.to(torch.int8)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
|
||||
k = 'cast_w'
|
||||
for _ in range(repeat // 2):
|
||||
newx = w.to(torch.int8)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
newx = w.to(torch.int8)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
time_standard = info['standard_fwd'] + info['standard_gx'] + info['standard_gw']
|
||||
time_rowwise = info['x_quantize_rowwise'] + info['g_quantize_rowwise'] + info['w_quantize_colwise_transpose'] + info['w_quantize_rowwise'] + info['standard_gw'] + info['rowwise_fwd'] + info['rowwise_bwd']
|
||||
time_global = info['x_quantize_rowwise'] + info['g_quantize_rowwise'] + info['w_quantize_global'] + info['w_quantize_global_transpose'] + info['standard_gw'] + info['global_fwd'] + info['global_bwd']
|
||||
|
||||
print('TOTAL STANDARD', time_standard)
|
||||
print('TOTAL ROWWISE', time_rowwise)
|
||||
print('TOTAL GLOBAL', time_global)
|
||||
|
||||
print('speedup', -100*(time_global - time_standard)/time_standard)
|
||||
|
||||
info['time_standard'] = time_standard
|
||||
info['time_rowwise'] = time_rowwise
|
||||
info['time_global'] = time_global
|
||||
|
||||
|
||||
|
||||
info_json = json.dumps(info)
|
||||
|
||||
|
||||
with open("tests/triton_tests/info.jsonl", "a") as file:
|
||||
file.write(info_json + "\n")
|
||||
|
|
@ -1,142 +0,0 @@
|
|||
{"repeat": 64, "batch_size": 1024, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 0.047907233238220215, "standard_gw": 0.04326179623603821, "standard_gx": 0.042986124753952026, "rowwise_fwd": 0.03902614116668701, "rowwise_bwd": 0.038955360651016235, "global_fwd": 0.03974884748458862, "global_bwd": 0.0391639769077301, "x_quantize_rowwise": 0.02619624137878418, "g_quantize_rowwise": 0.02695620059967041, "w_quantize_rowwise": 0.02631545066833496, "w_quantize_colwise_transpose": 0.08677691221237183, "w_quantize_global": 0.07359683513641357, "w_quantize_global_transpose": 0.08226558566093445, "cast_x": 0.007815659046173096, "cast_g": 0.016041100025177002, "cast_w": 0.01600012183189392, "time_standard": 0.13415515422821045, "time_rowwise": 0.28748810291290283, "time_global": 0.33118948340415955}
|
||||
{"repeat": 64, "batch_size": 1024, "dim_out": 1024, "dim_in": 4096, "wm": 4, "switch": true, "standard_fwd": 0.04236400127410889, "standard_gw": 0.04898756742477417, "standard_gx": 0.04731118679046631, "rowwise_fwd": 0.03933534026145935, "rowwise_bwd": 0.03947317600250244, "global_fwd": 0.03688037395477295, "global_bwd": 0.039167702198028564, "x_quantize_rowwise": 0.02533942461013794, "g_quantize_rowwise": 0.02516806125640869, "w_quantize_rowwise": 0.02528354525566101, "w_quantize_colwise_transpose": 0.0903792679309845, "w_quantize_global": 0.0997595489025116, "w_quantize_global_transpose": 0.10209530591964722, "cast_x": 0.01626834273338318, "cast_g": 0.011973083019256592, "cast_w": 0.016044825315475464, "time_standard": 0.13866275548934937, "time_rowwise": 0.2939663827419281, "time_global": 0.37739798426628113}
|
||||
{"repeat": 64, "batch_size": 2048, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 0.07753819227218628, "standard_gw": 0.08026883006095886, "standard_gx": 0.0906921923160553, "rowwise_fwd": 0.0630207359790802, "rowwise_bwd": 0.058263540267944336, "global_fwd": 0.06167963147163391, "global_bwd": 0.05801767110824585, "x_quantize_rowwise": 0.034205615520477295, "g_quantize_rowwise": 0.03341957926750183, "w_quantize_rowwise": 0.03244727849960327, "w_quantize_colwise_transpose": 0.08665025234222412, "w_quantize_global": 0.09483471512794495, "w_quantize_global_transpose": 0.10108202695846558, "cast_x": 0.012032687664031982, "cast_g": 0.03752484917640686, "cast_w": 0.01605972647666931, "time_standard": 0.24849921464920044, "time_rowwise": 0.3882758319377899, "time_global": 0.46350806951522827}
|
||||
{"repeat": 64, "batch_size": 2048, "dim_out": 1024, "dim_in": 4096, "wm": 4, "switch": true, "standard_fwd": 0.09099021553993225, "standard_gw": 0.0799819827079773, "standard_gx": 0.07644668221473694, "rowwise_fwd": 0.05840510129928589, "rowwise_bwd": 0.06359070539474487, "global_fwd": 0.057831406593322754, "global_bwd": 0.06148591637611389, "x_quantize_rowwise": 0.03434717655181885, "g_quantize_rowwise": 0.03361701965332031, "w_quantize_rowwise": 0.03209337592124939, "w_quantize_colwise_transpose": 0.09028613567352295, "w_quantize_global": 0.0944770872592926, "w_quantize_global_transpose": 0.0994168221950531, "cast_x": 0.03769621253013611, "cast_g": 0.012010335922241211, "cast_w": 0.01600012183189392, "time_standard": 0.24741888046264648, "time_rowwise": 0.39232149720191956, "time_global": 0.4611574113368988}
|
||||
{"repeat": 64, "batch_size": 4096, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 0.14450401067733765, "standard_gw": 0.14326348900794983, "standard_gx": 0.14762207865715027, "rowwise_fwd": 0.10525062680244446, "rowwise_bwd": 0.09800493717193604, "global_fwd": 0.10229647159576416, "global_bwd": 0.09718164801597595, "x_quantize_rowwise": 0.03429874777793884, "g_quantize_rowwise": 0.04567950963973999, "w_quantize_rowwise": 0.03365054726600647, "w_quantize_colwise_transpose": 0.08654966950416565, "w_quantize_global": 0.09663775563240051, "w_quantize_global_transpose": 0.10383129119873047, "cast_x": 0.01605972647666931, "cast_g": 0.08305534720420837, "cast_w": 0.01624971628189087, "time_standard": 0.43538957834243774, "time_rowwise": 0.5466975271701813, "time_global": 0.6231889128684998}
|
||||
{"repeat": 64, "batch_size": 4096, "dim_out": 1024, "dim_in": 4096, "wm": 4, "switch": true, "standard_fwd": 0.14496594667434692, "standard_gw": 0.1412704586982727, "standard_gx": 0.14446303248405457, "rowwise_fwd": 0.10041892528533936, "rowwise_bwd": 0.10674074292182922, "global_fwd": 0.09856373071670532, "global_bwd": 0.10319426655769348, "x_quantize_rowwise": 0.045571476221084595, "g_quantize_rowwise": 0.03273040056228638, "w_quantize_rowwise": 0.033464282751083374, "w_quantize_colwise_transpose": 0.09154900908470154, "w_quantize_global": 0.0964440405368805, "w_quantize_global_transpose": 0.1031048595905304, "cast_x": 0.0835023820400238, "cast_g": 0.016242265701293945, "cast_w": 0.016283243894577026, "time_standard": 0.4306994378566742, "time_rowwise": 0.5517452955245972, "time_global": 0.6208792328834534}
|
||||
{"repeat": 64, "batch_size": 8192, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 0.28106942772865295, "standard_gw": 0.2841465175151825, "standard_gx": 0.301852822303772, "rowwise_fwd": 0.19879266619682312, "rowwise_bwd": 0.16228482127189636, "global_fwd": 0.19488856196403503, "global_bwd": 0.1607760787010193, "x_quantize_rowwise": 0.033974647521972656, "g_quantize_rowwise": 0.08221715688705444, "w_quantize_rowwise": 0.03248825669288635, "w_quantize_colwise_transpose": 0.08646398782730103, "w_quantize_global": 0.0939294695854187, "w_quantize_global_transpose": 0.09895861148834229, "cast_x": 0.03753975033760071, "cast_g": 0.15900656580924988, "cast_w": 0.01603737473487854, "time_standard": 0.8670687675476074, "time_rowwise": 0.8803680539131165, "time_global": 0.9488910436630249}
|
||||
{"repeat": 64, "batch_size": 8192, "dim_out": 1024, "dim_in": 4096, "wm": 4, "switch": true, "standard_fwd": 0.26415660977363586, "standard_gw": 0.2679601311683655, "standard_gx": 0.30617788434028625, "rowwise_fwd": 0.180121511220932, "rowwise_bwd": 0.21555647253990173, "global_fwd": 0.17506256699562073, "global_bwd": 0.2116672694683075, "x_quantize_rowwise": 0.08289515972137451, "g_quantize_rowwise": 0.033795833587646484, "w_quantize_rowwise": 0.03366544842720032, "w_quantize_colwise_transpose": 0.09965524077415466, "w_quantize_global": 0.09595602750778198, "w_quantize_global_transpose": 0.1024976372718811, "cast_x": 0.1602955162525177, "cast_g": 0.03787502646446228, "cast_w": 0.016216188669204712, "time_standard": 0.8382946252822876, "time_rowwise": 0.9136497974395752, "time_global": 0.9698346257209778}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 0.5719438195228577, "standard_gw": 0.524863600730896, "standard_gx": 0.6005167961120605, "rowwise_fwd": 0.3750324249267578, "rowwise_bwd": 0.28166547417640686, "global_fwd": 0.3674700856208801, "global_bwd": 0.2798214554786682, "x_quantize_rowwise": 0.04655122756958008, "g_quantize_rowwise": 0.1555122435092926, "w_quantize_rowwise": 0.03437697887420654, "w_quantize_colwise_transpose": 0.08634477853775024, "w_quantize_global": 0.09759142994880676, "w_quantize_global_transpose": 0.10081753134727478, "cast_x": 0.0828765332698822, "cast_g": 0.31184032559394836, "cast_w": 0.016063451766967773, "time_standard": 1.6973242163658142, "time_rowwise": 1.5043467283248901, "time_global": 1.5726275742053986}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 1024, "dim_in": 4096, "wm": 4, "switch": true, "standard_fwd": 0.5423910915851593, "standard_gw": 0.5674734711647034, "standard_gx": 0.5907565355300903, "rowwise_fwd": 0.3149174153804779, "rowwise_bwd": 0.3899820148944855, "global_fwd": 0.2909451723098755, "global_bwd": 0.3783814609050751, "x_quantize_rowwise": 0.15584751963615417, "g_quantize_rowwise": 0.04688650369644165, "w_quantize_rowwise": 0.031463801860809326, "w_quantize_colwise_transpose": 0.09072571992874146, "w_quantize_global": 0.09774044156074524, "w_quantize_global_transpose": 0.10405108332633972, "cast_x": 0.3111511468887329, "cast_g": 0.08282437920570374, "cast_w": 0.015992671251296997, "time_standard": 1.700621098279953, "time_rowwise": 1.5972964465618134, "time_global": 1.6413256525993347}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 4096, "dim_in": 1024, "wm": 4, "switch": false, "standard_fwd": 1.2115389108657837, "standard_gw": 1.1259466409683228, "standard_gx": 1.1027492582798004, "rowwise_fwd": 0.7407031953334808, "rowwise_bwd": 0.5539208650588989, "global_fwd": 0.7214657962322235, "global_bwd": 0.5515590310096741, "x_quantize_rowwise": 0.08765608072280884, "g_quantize_rowwise": 0.3022328019142151, "w_quantize_rowwise": 0.03347545862197876, "w_quantize_colwise_transpose": 0.08694455027580261, "w_quantize_global": 0.09706243872642517, "w_quantize_global_transpose": 0.10102614760398865, "cast_x": 0.1592189073562622, "cast_g": 0.6166175007820129, "cast_w": 0.01607835292816162, "time_standard": 3.440234810113907, "time_rowwise": 2.930879592895508, "time_global": 2.986948937177658}
|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{"repeat": 64, "batch_size": 2048, "dim_out": 5120, "dim_in": 1280, "wm": 4, "switch": false, "standard_fwd": 0.11088326573371887, "standard_gw": 0.10994821786880493, "standard_gx": 0.12367218732833862, "rowwise_fwd": 0.07392093539237976, "rowwise_bwd": 0.07127970457077026, "global_fwd": 0.0730752944946289, "global_bwd": 0.07089227437973022, "x_quantize_rowwise": 0.03361701965332031, "g_quantize_rowwise": 0.03525242209434509, "w_quantize_rowwise": 0.03341585397720337, "w_quantize_colwise_transpose": 0.14318525791168213, "w_quantize_global": 0.09704753756523132, "w_quantize_global_transpose": 0.10221078991889954, "cast_x": 0.012002885341644287, "cast_g": 0.05240738391876221, "cast_w": 0.027313828468322754, "time_standard": 0.3445036709308624, "time_rowwise": 0.5006194114685059, "time_global": 0.5220435559749603}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 1408, "dim_in": 6144, "wm": 4.3637, "switch": true, "standard_fwd": 2.4625882506370544, "standard_gw": 2.421922981739044, "standard_gx": 2.380847930908203, "rowwise_fwd": 1.1231191456317902, "rowwise_bwd": 1.360483467578888, "global_fwd": 1.0947436094284058, "global_bwd": 1.3314113020896912, "x_quantize_rowwise": 0.4795975983142853, "g_quantize_rowwise": 0.11777132749557495, "w_quantize_rowwise": 0.02699345350265503, "w_quantize_colwise_transpose": 0.18484890460968018, "w_quantize_global": 0.07201358675956726, "w_quantize_global_transpose": 0.0803135335445404, "cast_x": 0.920858234167099, "cast_g": 0.21616369485855103, "cast_w": 0.03937259316444397, "time_standard": 7.265359163284302, "time_rowwise": 5.714736878871918, "time_global": 5.597773939371109}
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 5632, "dim_in": 1408, "wm": 4, "switch": false, "standard_fwd": 4.755370318889618, "standard_gw": 4.736289381980896, "standard_gx": 4.0378570556640625, "rowwise_fwd": 2.4783052504062653, "rowwise_bwd": 1.9634142518043518, "global_fwd": 2.435591071844101, "global_bwd": 1.9498206675052643, "x_quantize_rowwise": 0.22948533296585083, "g_quantize_rowwise": 0.9186491370201111, "w_quantize_rowwise": 0.028233975172042847, "w_quantize_colwise_transpose": 0.17858296632766724, "w_quantize_global": 0.07418543100357056, "w_quantize_global_transpose": 0.07958710193634033, "cast_x": 0.4257224500179291, "cast_g": 1.680031418800354, "cast_w": 0.03458559513092041, "time_standard": 13.529516756534576, "time_rowwise": 10.532960295677185, "time_global": 10.423608124256134}
|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 1408, "dim_in": 5632, "wm": 4, "switch": true, "standard_fwd": 4.050172865390778, "standard_gw": 3.916766494512558, "standard_gx": 4.281226545572281, "rowwise_fwd": 1.9789263606071472, "rowwise_bwd": 2.477586269378662, "global_fwd": 1.9495487213134766, "global_bwd": 2.434592694044113, "x_quantize_rowwise": 0.918261706829071, "g_quantize_rowwise": 0.22961944341659546, "w_quantize_rowwise": 0.025540590286254883, "w_quantize_colwise_transpose": 0.17032772302627563, "w_quantize_global": 0.07384642958641052, "w_quantize_global_transpose": 0.08105114102363586, "cast_x": 1.679886132478714, "cast_g": 0.42508915066719055, "cast_w": 0.03442913293838501, "time_standard": 12.248165905475616, "time_rowwise": 9.717028588056564, "time_global": 9.60368663072586}
|
||||
{"repeat": 64, "batch_size": 131072, "dim_out": 5632, "dim_in": 1408, "wm": 4, "switch": false, "standard_fwd": 9.53347235918045, "standard_gw": 8.138865232467651, "standard_gx": 7.9666972160339355, "rowwise_fwd": 4.984956234693527, "rowwise_bwd": 3.850068897008896, "global_fwd": 4.9025751650333405, "global_bwd": 3.820303827524185, "x_quantize_rowwise": 0.45222043991088867, "g_quantize_rowwise": 1.8290691077709198, "w_quantize_rowwise": 0.026736408472061157, "w_quantize_colwise_transpose": 0.17832592129707336, "w_quantize_global": 0.07471069693565369, "w_quantize_global_transpose": 0.08177757263183594, "cast_x": 0.8435025811195374, "cast_g": 3.3529214560985565, "cast_w": 0.03475695848464966, "time_standard": 25.639034807682037, "time_rowwise": 19.460242241621017, "time_global": 19.299522042274475}
|
||||
{"repeat": 64, "batch_size": 131072, "dim_out": 1408, "dim_in": 5632, "wm": 4, "switch": true, "standard_fwd": 7.996037602424622, "standard_gw": 8.2748644053936, "standard_gx": 8.523400872945786, "rowwise_fwd": 3.8556940853595734, "rowwise_bwd": 4.966288805007935, "global_fwd": 3.820043057203293, "global_bwd": 4.882067441940308, "x_quantize_rowwise": 1.8279887735843658, "g_quantize_rowwise": 0.4520900547504425, "w_quantize_rowwise": 0.02676248550415039, "w_quantize_colwise_transpose": 0.17083808779716492, "w_quantize_global": 0.07691606879234314, "w_quantize_global_transpose": 0.08223950862884521, "cast_x": 3.3530443906784058, "cast_g": 0.8434318006038666, "cast_w": 0.034671276807785034, "time_standard": 24.794302880764008, "time_rowwise": 19.574526697397232, "time_global": 19.416209310293198}
|
||||
{"repeat": 64, "batch_size": 1024, "dim_out": 6656, "dim_in": 1664, "wm": 4, "switch": false, "standard_fwd": 0.09413063526153564, "standard_gw": 0.10038167238235474, "standard_gx": 0.09725615382194519, "rowwise_fwd": 0.05979463458061218, "rowwise_bwd": 0.0525452196598053, "global_fwd": 0.059057027101516724, "global_bwd": 0.05194917321205139, "x_quantize_rowwise": 0.02664700150489807, "g_quantize_rowwise": 0.02642720937728882, "w_quantize_rowwise": 0.030562281608581543, "w_quantize_colwise_transpose": 0.2400912344455719, "w_quantize_global": 0.09407848119735718, "w_quantize_global_transpose": 0.10256841778755188, "cast_x": 0.008724629878997803, "cast_g": 0.028502196073532104, "cast_w": 0.05552172660827637, "time_standard": 0.29176846146583557, "time_rowwise": 0.5364492535591125, "time_global": 0.4611089825630188}
|
||||
{"repeat": 64, "batch_size": 1024, "dim_out": 1664, "dim_in": 6656, "wm": 4, "switch": true, "standard_fwd": 0.09753555059432983, "standard_gw": 0.10102242231369019, "standard_gx": 0.09121373295783997, "rowwise_fwd": 0.052150338888168335, "rowwise_bwd": 0.059779733419418335, "global_fwd": 0.05161017179489136, "global_bwd": 0.05943328142166138, "x_quantize_rowwise": 0.026702880859375, "g_quantize_rowwise": 0.02469494938850403, "w_quantize_rowwise": 0.03324449062347412, "w_quantize_colwise_transpose": 0.23468583822250366, "w_quantize_global": 0.09394437074661255, "w_quantize_global_transpose": 0.10142102837562561, "cast_x": 0.028360635042190552, "cast_g": 0.008717179298400879, "cast_w": 0.05577504634857178, "time_standard": 0.28977170586586, "time_rowwise": 0.5322806537151337, "time_global": 0.4588291049003601}
|
||||
{"repeat": 64, "batch_size": 2048, "dim_out": 6656, "dim_in": 1664, "wm": 4, "switch": false, "standard_fwd": 0.18056854605674744, "standard_gw": 0.18374621868133545, "standard_gx": 0.19219890236854553, "rowwise_fwd": 0.1150965690612793, "rowwise_bwd": 0.0903494656085968, "global_fwd": 0.11263042688369751, "global_bwd": 0.08984282612800598, "x_quantize_rowwise": 0.027067959308624268, "g_quantize_rowwise": 0.040043145418167114, "w_quantize_rowwise": 0.03063306212425232, "w_quantize_colwise_transpose": 0.24128705263137817, "w_quantize_global": 0.09361281991004944, "w_quantize_global_transpose": 0.1024976372718811, "cast_x": 0.01381710171699524, "cast_g": 0.06845593452453613, "cast_w": 0.05572289228439331, "time_standard": 0.5565136671066284, "time_rowwise": 0.7282234728336334, "time_global": 0.6494410336017609}
|
||||
{"repeat": 64, "batch_size": 2048, "dim_out": 1664, "dim_in": 6656, "wm": 4, "switch": true, "standard_fwd": 0.16536936163902283, "standard_gw": 0.19479170441627502, "standard_gx": 0.18597766757011414, "rowwise_fwd": 0.09634345769882202, "rowwise_bwd": 0.11937320232391357, "global_fwd": 0.09264424443244934, "global_bwd": 0.11524930596351624, "x_quantize_rowwise": 0.04038214683532715, "g_quantize_rowwise": 0.025559216737747192, "w_quantize_rowwise": 0.03334507346153259, "w_quantize_colwise_transpose": 0.23956596851348877, "w_quantize_global": 0.09445473551750183, "w_quantize_global_transpose": 0.1020580530166626, "cast_x": 0.06891414523124695, "cast_g": 0.013861805200576782, "cast_w": 0.05607306957244873, "time_standard": 0.546138733625412, "time_rowwise": 0.7493607699871063, "time_global": 0.6651394069194794}
|
||||
{"repeat": 64, "batch_size": 4096, "dim_out": 6656, "dim_in": 1664, "wm": 4, "switch": false, "standard_fwd": 0.36064907908439636, "standard_gw": 0.3711991012096405, "standard_gx": 0.3863237798213959, "rowwise_fwd": 0.22270530462265015, "rowwise_bwd": 0.1760348677635193, "global_fwd": 0.21781772375106812, "global_bwd": 0.17484650015830994, "x_quantize_rowwise": 0.02625212073326111, "g_quantize_rowwise": 0.07131323218345642, "w_quantize_rowwise": 0.030372291803359985, "w_quantize_colwise_transpose": 0.23974105715751648, "w_quantize_global": 0.09407475590705872, "w_quantize_global_transpose": 0.1024492084980011, "cast_x": 0.028584152460098267, "cast_g": 0.1303069293498993, "cast_w": 0.05582347512245178, "time_standard": 1.1181719601154327, "time_rowwise": 1.137617975473404, "time_global": 1.057952642440796}
|
||||
{"repeat": 64, "batch_size": 4096, "dim_out": 1664, "dim_in": 6656, "wm": 4, "switch": true, "standard_fwd": 0.32703205943107605, "standard_gw": 0.3764517605304718, "standard_gx": 0.3938935697078705, "rowwise_fwd": 0.18771737813949585, "rowwise_bwd": 0.2374798059463501, "global_fwd": 0.1843757927417755, "global_bwd": 0.23005902767181396, "x_quantize_rowwise": 0.07155537605285645, "g_quantize_rowwise": 0.02625212073326111, "w_quantize_rowwise": 0.03294646739959717, "w_quantize_colwise_transpose": 0.23755058646202087, "w_quantize_global": 0.09388476610183716, "w_quantize_global_transpose": 0.10246038436889648, "cast_x": 0.13131648302078247, "cast_g": 0.028781592845916748, "cast_w": 0.05638599395751953, "time_standard": 1.0973773896694183, "time_rowwise": 1.1699534952640533, "time_global": 1.0850392282009125}
|
||||
{"repeat": 64, "batch_size": 8192, "dim_out": 6656, "dim_in": 1664, "wm": 4, "switch": false, "standard_fwd": 0.7961541414260864, "standard_gw": 0.7424280047416687, "standard_gx": 0.8688867092132568, "rowwise_fwd": 0.432576984167099, "rowwise_bwd": 0.34543126821517944, "global_fwd": 0.4248805344104767, "global_bwd": 0.3432855010032654, "x_quantize_rowwise": 0.03750622272491455, "g_quantize_rowwise": 0.13292208313941956, "w_quantize_rowwise": 0.030599534511566162, "w_quantize_colwise_transpose": 0.24292618036270142, "w_quantize_global": 0.09351596236228943, "w_quantize_global_transpose": 0.1026056706905365, "cast_x": 0.06843730807304382, "cast_g": 0.2539418637752533, "cast_w": 0.05568563938140869, "time_standard": 2.407468855381012, "time_rowwise": 1.9643902778625488, "time_global": 1.8771439790725708}
|
||||
{"repeat": 64, "batch_size": 8192, "dim_out": 1664, "dim_in": 6656, "wm": 4, "switch": true, "standard_fwd": 0.7150471210479736, "standard_gw": 0.7525831460952759, "standard_gx": 0.8075274527072906, "rowwise_fwd": 0.36595389246940613, "rowwise_bwd": 0.4404708743095398, "global_fwd": 0.3485158085823059, "global_bwd": 0.4275962710380554, "x_quantize_rowwise": 0.1329965889453888, "g_quantize_rowwise": 0.03767386078834534, "w_quantize_rowwise": 0.03295019268989563, "w_quantize_colwise_transpose": 0.23509934544563293, "w_quantize_global": 0.09398534893989563, "w_quantize_global_transpose": 0.10186433792114258, "cast_x": 0.2537667751312256, "cast_g": 0.06839632987976074, "cast_w": 0.05571544170379639, "time_standard": 2.27515771985054, "time_rowwise": 1.9977279007434845, "time_global": 1.8952153623104095}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 6656, "dim_in": 1664, "wm": 4, "switch": false, "standard_fwd": 1.6392990946769714, "standard_gw": 1.4941170811653137, "standard_gx": 1.4451220631599426, "rowwise_fwd": 0.8369758725166321, "rowwise_bwd": 0.6830468773841858, "global_fwd": 0.8197203278541565, "global_bwd": 0.6782263517379761, "x_quantize_rowwise": 0.06883591413497925, "g_quantize_rowwise": 0.2565309405326843, "w_quantize_rowwise": 0.03046169877052307, "w_quantize_colwise_transpose": 0.2430342137813568, "w_quantize_global": 0.09346380829811096, "w_quantize_global_transpose": 0.10301917791366577, "cast_x": 0.13044849038124084, "cast_g": 0.5010999739170074, "cast_w": 0.05590170621871948, "time_standard": 4.578538239002228, "time_rowwise": 3.613002598285675, "time_global": 3.5139136016368866}
|
||||
{"repeat": 64, "batch_size": 16384, "dim_out": 1664, "dim_in": 6656, "wm": 4, "switch": true, "standard_fwd": 1.4654621481895447, "standard_gw": 1.5012174844741821, "standard_gx": 1.5183314681053162, "rowwise_fwd": 0.7059797644615173, "rowwise_bwd": 0.8470229804515839, "global_fwd": 0.6788894534111023, "global_bwd": 0.8200779557228088, "x_quantize_rowwise": 0.2564750611782074, "g_quantize_rowwise": 0.06899237632751465, "w_quantize_rowwise": 0.03293529152870178, "w_quantize_colwise_transpose": 0.23559853434562683, "w_quantize_global": 0.09375810623168945, "w_quantize_global_transpose": 0.10203942656517029, "cast_x": 0.5010105669498444, "cast_g": 0.13037025928497314, "cast_w": 0.05577504634857178, "time_standard": 4.485011100769043, "time_rowwise": 3.648221492767334, "time_global": 3.521449863910675}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 6656, "dim_in": 1664, "wm": 4, "switch": false, "standard_fwd": 3.236088901758194, "standard_gw": 2.8601549565792084, "standard_gx": 2.8000958263874054, "rowwise_fwd": 1.6548968851566315, "rowwise_bwd": 1.3559646904468536, "global_fwd": 1.6249343752861023, "global_bwd": 1.3474412262439728, "x_quantize_rowwise": 0.13122707605361938, "g_quantize_rowwise": 0.5038455128669739, "w_quantize_rowwise": 0.03061816096305847, "w_quantize_colwise_transpose": 0.24301931262016296, "w_quantize_global": 0.09343400597572327, "w_quantize_global_transpose": 0.10178983211517334, "cast_x": 0.25383010506629944, "cast_g": 0.9955987334251404, "cast_w": 0.05569681525230408, "time_standard": 8.896339684724808, "time_rowwise": 6.779726594686508, "time_global": 6.662826985120773}
|
||||
{"repeat": 64, "batch_size": 32768, "dim_out": 1664, "dim_in": 6656, "wm": 4, "switch": true, "standard_fwd": 2.8433389961719513, "standard_gw": 2.861086279153824, "standard_gx": 3.0227042734622955, "rowwise_fwd": 1.4057457447052002, "rowwise_bwd": 1.6565024852752686, "global_fwd": 1.3475008308887482, "global_bwd": 1.6247481107711792, "x_quantize_rowwise": 0.5038045346736908, "g_quantize_rowwise": 0.13130158185958862, "w_quantize_rowwise": 0.03298744559288025, "w_quantize_colwise_transpose": 0.23539364337921143, "w_quantize_global": 0.09393692016601562, "w_quantize_global_transpose": 0.10208785533905029, "cast_x": 0.9952597320079803, "cast_g": 0.25385990738868713, "cast_w": 0.05589798092842102, "time_standard": 8.72712954878807, "time_rowwise": 6.826821714639664, "time_global": 6.664466112852097}
|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 6656, "dim_in": 1664, "wm": 4, "switch": false, "standard_fwd": 6.449159234762192, "standard_gw": 6.384443491697311, "standard_gx": 5.543403327465057, "rowwise_fwd": 3.3065229654312134, "rowwise_bwd": 2.6249960064888, "global_fwd": 3.2497718930244446, "global_bwd": 2.6061534881591797, "x_quantize_rowwise": 0.25821104645729065, "g_quantize_rowwise": 0.9981803596019745, "w_quantize_rowwise": 0.030606985092163086, "w_quantize_colwise_transpose": 0.24094432592391968, "w_quantize_global": 0.09358301758766174, "w_quantize_global_transpose": 0.10264664888381958, "cast_x": 0.5018562078475952, "cast_g": 1.9840113818645477, "cast_w": 0.05584210157394409, "time_standard": 18.37700605392456, "time_rowwise": 13.843905180692673, "time_global": 13.692989945411682}
|
||||
{"repeat": 64, "batch_size": 65536, "dim_out": 1664, "dim_in": 6656, "wm": 4, "switch": true, "standard_fwd": 5.508493632078171, "standard_gw": 5.689781159162521, "standard_gx": 6.020743399858475, "rowwise_fwd": 2.640843391418457, "rowwise_bwd": 3.3075474202632904, "global_fwd": 2.605751156806946, "global_bwd": 3.2674334943294525, "x_quantize_rowwise": 0.9983181953430176, "g_quantize_rowwise": 0.25597214698791504, "w_quantize_rowwise": 0.03277510404586792, "w_quantize_colwise_transpose": 0.23587048053741455, "w_quantize_global": 0.09367987513542175, "w_quantize_global_transpose": 0.10236725211143494, "cast_x": 1.9848868250846863, "cast_g": 0.5010329186916351, "cast_w": 0.055771321058273315, "time_standard": 17.219018191099167, "time_rowwise": 13.161107897758484, "time_global": 13.013303279876709}
|
||||
{"repeat": 64, "batch_size": 131072, "dim_out": 6656, "dim_in": 1664, "wm": 4, "switch": false, "standard_fwd": 12.975204735994339, "standard_gw": 11.424731463193893, "standard_gx": 11.05477660894394, "rowwise_fwd": 6.623122841119766, "rowwise_bwd": 5.253363400697708, "global_fwd": 6.506938487291336, "global_bwd": 5.211424082517624, "x_quantize_rowwise": 0.5057789385318756, "g_quantize_rowwise": 1.9870363175868988, "w_quantize_rowwise": 0.030517578125, "w_quantize_colwise_transpose": 0.24361908435821533, "w_quantize_global": 0.09384006261825562, "w_quantize_global_transpose": 0.10285153985023499, "cast_x": 0.9967051446437836, "cast_g": 3.9620958268642426, "cast_w": 0.05599111318588257, "time_standard": 35.45471280813217, "time_rowwise": 26.068169623613358, "time_global": 25.83260089159012}
|
||||
{"repeat": 64, "batch_size": 131072, "dim_out": 1664, "dim_in": 6656, "wm": 4, "switch": true, "standard_fwd": 11.05555146932602, "standard_gw": 11.32136583328247, "standard_gx": 12.035444378852844, "rowwise_fwd": 5.243867635726929, "rowwise_bwd": 6.622854620218277, "global_fwd": 5.209986120462418, "global_bwd": 6.507329642772675, "x_quantize_rowwise": 1.9862838089466095, "g_quantize_rowwise": 0.506080687046051, "w_quantize_rowwise": 0.03318488597869873, "w_quantize_colwise_transpose": 0.23682788014411926, "w_quantize_global": 0.09349361062049866, "w_quantize_global_transpose": 0.1023709774017334, "cast_x": 3.962486982345581, "cast_g": 0.9956248104572296, "cast_w": 0.05572289228439331, "time_standard": 34.412361681461334, "time_rowwise": 25.950465351343155, "time_global": 25.726910680532455}
|
||||
|
|
@ -1,20 +0,0 @@
|
|||
{"repeat": 32, "batch_size": 16384, "dim": 1024, "standard": 3.807276487350464, "my_standard": 4.196919500827789, "standard_compiled": 3.771558403968811, "sb": 3.5132691264152527}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1024, "standard": 7.215872406959534, "my_standard": 7.991522550582886, "standard_compiled": 7.241688668727875, "sb": 6.581142544746399}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1024, "standard": 14.26444947719574, "my_standard": 15.685759484767914, "standard_compiled": 14.251746237277985, "sb": 12.735314667224884}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1024, "standard": 28.49559485912323, "my_standard": 31.26966953277588, "standard_compiled": 28.414390981197357, "sb": 25.319166481494904}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1280, "standard": 5.887262523174286, "my_standard": 6.132654845714569, "standard_compiled": 5.902409553527832, "sb": 4.947789013385773}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1280, "standard": 11.14131510257721, "my_standard": 12.859955430030823, "standard_compiled": 11.133037507534027, "sb": 9.303092956542969}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1280, "standard": 22.193141281604767, "my_standard": 25.66336840391159, "standard_compiled": 22.22583442926407, "sb": 18.285617232322693}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1280, "standard": 44.23898458480835, "my_standard": 51.30268633365631, "standard_compiled": 44.08355802297592, "sb": 35.999126732349396}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1408, "standard": 6.938718259334564, "my_standard": 7.269218564033508, "standard_compiled": 6.94604218006134, "sb": 5.764961242675781}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1408, "standard": 13.04878294467926, "my_standard": 13.742901384830475, "standard_compiled": 13.011425733566284, "sb": 10.774023830890656}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1408, "standard": 26.738539338111877, "my_standard": 27.739346027374268, "standard_compiled": 26.75659954547882, "sb": 21.882005035877228}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1408, "standard": 51.905401051044464, "my_standard": 53.98637801408768, "standard_compiled": 51.8316924571991, "sb": 41.67725890874863}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1664, "standard": 9.233824908733368, "my_standard": 9.619377553462982, "standard_compiled": 9.214423596858978, "sb": 7.557623088359833}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1664, "standard": 17.324909567832947, "my_standard": 17.996780574321747, "standard_compiled": 17.29544997215271, "sb": 14.035224914550781}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1664, "standard": 35.51657497882843, "my_standard": 36.674730479717255, "standard_compiled": 35.43049842119217, "sb": 28.38330715894699}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1664, "standard": 69.0087378025055, "my_standard": 71.56594842672348, "standard_compiled": 68.82885098457336, "sb": 54.01633679866791}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 2048, "standard": 12.590140104293823, "my_standard": 13.106442987918854, "standard_compiled": 12.606985867023468, "sb": 10.286301374435425}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 2048, "standard": 24.830535054206848, "my_standard": 25.563716888427734, "standard_compiled": 24.895809590816498, "sb": 19.559212028980255}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 2048, "standard": 49.55078661441803, "my_standard": 51.16480588912964, "standard_compiled": 49.739621579647064, "sb": 38.29141706228256}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 2048, "standard": 98.36294502019882, "my_standard": 102.69322991371155, "standard_compiled": 98.76712411642075, "sb": 75.88706165552139}
|
||||
|
|
@ -1,20 +0,0 @@
|
|||
{"repeat": 32, "batch_size": 16384, "dim": 1024, "standard": 4.91420179605484, "my_standard": 5.577877163887024, "standard_compiled": 4.810944199562073, "sb": 4.512995481491089}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1024, "standard": 8.876129984855652, "my_standard": 10.154612362384796, "standard_compiled": 8.820965886116028, "sb": 8.367843925952911}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1024, "standard": 17.47015118598938, "my_standard": 19.857674837112427, "standard_compiled": 17.338842153549194, "sb": 15.992552042007446}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1024, "standard": 34.824438393116, "my_standard": 39.499424397945404, "standard_compiled": 34.56207364797592, "sb": 31.573951244354248}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1280, "standard": 7.342606782913208, "my_standard": 7.9323723912239075, "standard_compiled": 7.279552519321442, "sb": 6.395488977432251}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1280, "standard": 13.69999349117279, "my_standard": 16.0503089427948, "standard_compiled": 13.603456318378448, "sb": 11.813104152679443}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1280, "standard": 29.557034373283386, "my_standard": 34.2303067445755, "standard_compiled": 29.382556676864624, "sb": 22.882774472236633}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1280, "standard": 53.629085421562195, "my_standard": 63.07622790336609, "standard_compiled": 53.33048850297928, "sb": 44.76426541805267}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1408, "standard": 8.81417840719223, "my_standard": 9.477965533733368, "standard_compiled": 8.73943418264389, "sb": 7.479414343833923}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1408, "standard": 16.242466866970062, "my_standard": 17.616644501686096, "standard_compiled": 16.14125818014145, "sb": 13.665586709976196}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1408, "standard": 32.429613173007965, "my_standard": 34.80646014213562, "standard_compiled": 32.319076359272, "sb": 27.123987674713135}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1408, "standard": 62.85770237445831, "my_standard": 67.55391508340836, "standard_compiled": 62.453076243400574, "sb": 51.53566598892212}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1664, "standard": 11.585861444473267, "my_standard": 12.565858662128448, "standard_compiled": 11.504307389259338, "sb": 9.657211601734161}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1664, "standard": 21.261662244796753, "my_standard": 22.771358489990234, "standard_compiled": 21.12410217523575, "sb": 17.64291524887085}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1664, "standard": 42.85307973623276, "my_standard": 45.70870101451874, "standard_compiled": 42.57970303297043, "sb": 34.918561577796936}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1664, "standard": 83.56057852506638, "my_standard": 89.11971747875214, "standard_compiled": 83.05662125349045, "sb": 66.32210314273834}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 2048, "standard": 15.7279372215271, "my_standard": 16.854502260684967, "standard_compiled": 15.655294060707092, "sb": 13.228952884674072}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 2048, "standard": 30.42648732662201, "my_standard": 32.26502239704132, "standard_compiled": 30.239209532737732, "sb": 24.354808032512665}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 2048, "standard": 60.779355466365814, "my_standard": 64.11923468112946, "standard_compiled": 60.89268624782562, "sb": 46.91776633262634}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 2048, "standard": 119.93677169084549, "my_standard": 128.19699943065643, "standard_compiled": 120.20225822925568, "sb": 92.3452153801918}
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
{"repeat": 32, "batch_size": 16384, "dim": 1024, "standard": 5.171686410903931, "my_standard": 5.839601159095764, "standard_compiled": 5.032263696193695, "sb": 4.89344447851181}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1024, "standard": 9.605035185813904, "my_standard": 10.910414159297943, "standard_compiled": 9.230785071849823, "sb": 9.128175675868988}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1024, "standard": 18.802084028720856, "my_standard": 21.311581134796143, "standard_compiled": 18.105976283550262, "sb": 17.489850521087646}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1024, "standard": 37.49683499336243, "my_standard": 42.40527004003525, "standard_compiled": 36.13145649433136, "sb": 34.58733111619949}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1280, "standard": 7.709823548793793, "my_standard": 8.290477097034454, "standard_compiled": 7.564418017864227, "sb": 6.8823546171188354}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1280, "standard": 14.64156061410904, "my_standard": 16.996942460536957, "standard_compiled": 14.4081711769104, "sb": 12.761622667312622}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1280, "standard": 31.40200674533844, "my_standard": 36.074504256248474, "standard_compiled": 30.981406569480896, "sb": 24.76389706134796}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1280, "standard": 56.93405121564865, "my_standard": 66.35250151157379, "standard_compiled": 56.07586354017258, "sb": 48.49743843078613}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1408, "standard": 9.188003838062286, "my_standard": 9.84550267457962, "standard_compiled": 9.006097912788391, "sb": 7.9473331570625305}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1408, "standard": 17.268165946006775, "my_standard": 18.64910125732422, "standard_compiled": 16.983114182949066, "sb": 14.70106840133667}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1408, "standard": 34.39047932624817, "my_standard": 36.69705241918564, "standard_compiled": 33.8401272892952, "sb": 29.188089072704315}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1408, "standard": 66.70494377613068, "my_standard": 71.27603143453598, "standard_compiled": 65.56134670972824, "sb": 55.6538850069046}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 1664, "standard": 12.10707426071167, "my_standard": 12.931793928146362, "standard_compiled": 11.76995038986206, "sb": 10.228671133518219}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 1664, "standard": 22.5130096077919, "my_standard": 23.962542414665222, "standard_compiled": 21.997176110744476, "sb": 18.89890432357788}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 1664, "standard": 45.210108160972595, "my_standard": 47.94136434793472, "standard_compiled": 44.2262664437294, "sb": 37.37735003232956}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 1664, "standard": 88.1955549120903, "my_standard": 93.6831533908844, "standard_compiled": 86.33609116077423, "sb": 71.23208791017532}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 2048, "standard": 16.538940370082855, "my_standard": 17.607316374778748, "standard_compiled": 16.108587384223938, "sb": 14.030493795871735}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 2048, "standard": 31.795650720596313, "my_standard": 33.57230871915817, "standard_compiled": 31.04180097579956, "sb": 25.971196591854095}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 2048, "standard": 63.021354377269745, "my_standard": 66.8477788567543, "standard_compiled": 61.682507395744324, "sb": 50.138771533966064}
|
||||
{"repeat": 32, "batch_size": 131072, "dim": 2048, "standard": 125.17062574625015, "my_standard": 133.60925763845444, "standard_compiled": 122.21191823482513, "sb": 98.40084612369537}
|
||||
{"repeat": 32, "batch_size": 16384, "dim": 4096, "standard": 57.31645971536636, "my_standard": 60.84543466567993, "standard_compiled": 55.78199774026871, "sb": 45.43223977088928}
|
||||
{"repeat": 32, "batch_size": 32768, "dim": 4096, "standard": 111.80306226015091, "my_standard": 119.0284714102745, "standard_compiled": 108.91905426979065, "sb": 85.4572057723999}
|
||||
{"repeat": 32, "batch_size": 65536, "dim": 4096, "standard": 220.4471081495285, "my_standard": 233.0927476286888, "standard_compiled": 214.26431089639664, "sb": 163.30372542142868}
|
||||
|
|
@ -1,64 +0,0 @@
|
|||
|
||||
import time
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import bitsandbytes.nn as bnn
|
||||
from bitsandbytes.nn.triton_based_modules import SwitchBackLinear, SwitchBackGlobalLinear, StandardLinear
|
||||
|
||||
def construct_model(dim, layers, module):
|
||||
modules = []
|
||||
for _ in range(layers):
|
||||
modules.append(module(dim, 4*dim))
|
||||
modules.append(module(4*dim, dim))
|
||||
return nn.Sequential(*modules).cuda().train()
|
||||
|
||||
def get_time(model, x, name):
|
||||
for _ in range(repeat // 2):
|
||||
#with torch.cuda.amp.autocast():
|
||||
out = model(x)
|
||||
#(2**16 * out.pow(2).mean()).backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
# with torch.cuda.amp.autocast():
|
||||
out = model(x)
|
||||
#(2**16 * out.pow(2).mean()).backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
print(f"time {name}: {(end - start) / repeat * 1000:.3f} ms")
|
||||
|
||||
if __name__ == '__main__':
|
||||
torch.manual_seed(0)
|
||||
|
||||
# hparams
|
||||
repeat = 16
|
||||
dim=2048
|
||||
layers =4
|
||||
batch_size = 2
|
||||
sequence_length = 2**15
|
||||
|
||||
# construct models
|
||||
standard = construct_model(dim, layers, nn.Linear).half()
|
||||
my_standard = construct_model(dim, layers, StandardLinear).half()
|
||||
switchback = construct_model(dim, layers, SwitchBackLinear).half()
|
||||
switchback_global = construct_model(dim, layers, SwitchBackGlobalLinear).half()
|
||||
#bnb_8bitmixed = construct_model(dim, layers, bnn.Linear8bitLt)
|
||||
|
||||
# simulate forward pass
|
||||
x = torch.randn(batch_size * sequence_length, dim, dtype=torch.float16).cuda()
|
||||
|
||||
# get time for forward and backward
|
||||
get_time(standard, x, "standard")
|
||||
get_time(my_standard, x, "my_standard")
|
||||
get_time(switchback, x, "switchback")
|
||||
get_time(switchback_global, x, "switchback_global")
|
||||
#get_time(bnb_8bitmixed, x, "bnb_8bitmixed")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,166 +0,0 @@
|
|||
|
||||
import torch
|
||||
import json
|
||||
from bitsandbytes.nn.triton_based_modules import SwitchBackGlobalMLP, SwitchBackGlobalLinear, StandardLinear
|
||||
import time
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
print('Startin')
|
||||
|
||||
|
||||
for dim in [1024, 1280, 1408, 1664, 2048]:
|
||||
for batch in [2**14, 2**15, 2**16, 2**17]:
|
||||
|
||||
if dim != 4096 or batch != 2**17:
|
||||
continue
|
||||
|
||||
|
||||
x1 = torch.randn(batch, dim).cuda().requires_grad_(True)
|
||||
d = 2
|
||||
|
||||
standard = torch.nn.Sequential(
|
||||
torch.nn.Linear(dim, 4 * dim),
|
||||
torch.nn.GELU(),
|
||||
torch.nn.Linear(4 * dim, dim),
|
||||
).cuda()
|
||||
|
||||
my_standard = torch.nn.Sequential(
|
||||
StandardLinear(dim, 4 * dim),
|
||||
torch.nn.GELU(),
|
||||
StandardLinear(4 * dim, dim),
|
||||
).cuda()
|
||||
|
||||
fused_mlp = SwitchBackGlobalMLP(dim, 4 * dim).cuda()
|
||||
|
||||
sb = torch.nn.Sequential(
|
||||
SwitchBackGlobalLinear(dim, 4 * dim),
|
||||
torch.nn.GELU(),
|
||||
SwitchBackGlobalLinear(4 * dim, dim),
|
||||
).cuda()
|
||||
|
||||
standard_compiled = torch.compile(standard)
|
||||
|
||||
print('Model part 2')
|
||||
|
||||
repeat = 32
|
||||
|
||||
|
||||
info = {'repeat' : repeat, 'batch_size' : batch, 'dim' : dim}
|
||||
|
||||
# k = 'standard'
|
||||
# for _ in range(repeat // 2):
|
||||
# with torch.cuda.amp.autocast():
|
||||
# out_standard = standard(x1)
|
||||
# ((2 ** 16) * out_standard).abs().mean().backward()
|
||||
|
||||
# torch.cuda.synchronize()
|
||||
# start = time.time()
|
||||
# for _ in range(repeat):
|
||||
# with torch.cuda.amp.autocast():
|
||||
# out_standard = standard(x1)
|
||||
# ((2 ** 16) * out_standard).abs().mean().backward()
|
||||
|
||||
# torch.cuda.synchronize()
|
||||
# end = time.time()
|
||||
# ms = (end - start) / repeat * 1000
|
||||
# print(f"time {k}: {ms:.3f} ms")
|
||||
# info[k] = ms
|
||||
|
||||
|
||||
# x1.grad.zero_()
|
||||
|
||||
# k = 'my_standard'
|
||||
# for _ in range(repeat // 2):
|
||||
# with torch.cuda.amp.autocast():
|
||||
# out_my_standard = my_standard(x1)
|
||||
# ((2 ** 16) * out_my_standard).abs().mean().backward()
|
||||
|
||||
# torch.cuda.synchronize()
|
||||
# start = time.time()
|
||||
# for _ in range(repeat):
|
||||
# with torch.cuda.amp.autocast():
|
||||
# out_my_standard = my_standard(x1)
|
||||
# ((2 ** 16) * out_my_standard).abs().mean().backward()
|
||||
|
||||
# torch.cuda.synchronize()
|
||||
# end = time.time()
|
||||
# ms = (end - start) / repeat * 1000
|
||||
# print(f"time {k}: {ms:.3f} ms")
|
||||
# info[k] = ms
|
||||
|
||||
# x1.grad.zero_()
|
||||
|
||||
# k = 'standard_compiled'
|
||||
# for _ in range(repeat // 2):
|
||||
# with torch.cuda.amp.autocast():
|
||||
# out_standard_compiled = standard_compiled(x1)
|
||||
# ((2 ** 16) * out_standard_compiled).abs().mean().backward()
|
||||
|
||||
# torch.cuda.synchronize()
|
||||
# start = time.time()
|
||||
# for _ in range(repeat):
|
||||
# with torch.cuda.amp.autocast():
|
||||
# out_standard_compiled = standard_compiled(x1)
|
||||
# ((2 ** 16) * out_standard_compiled).abs().mean().backward()
|
||||
|
||||
# torch.cuda.synchronize()
|
||||
# end = time.time()
|
||||
# ms = (end - start) / repeat * 1000
|
||||
# print(f"time {k}: {ms:.3f} ms")
|
||||
# info[k] = ms
|
||||
|
||||
# x1.grad.zero_()
|
||||
|
||||
k = 'sb'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_sb = sb(x1)
|
||||
((2 ** 16) * out_sb).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_sb = sb(x1)
|
||||
((2 ** 16) * out_sb).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
info_json = json.dumps(info)
|
||||
|
||||
|
||||
with open("tests/triton_tests/info_mlp_autocast.jsonl", "a") as file:
|
||||
file.write(info_json + "\n")
|
||||
|
||||
|
||||
#exit()
|
||||
|
||||
# err_fused = (out_standard - out_fused).abs().mean()
|
||||
# err_sb = (out_standard - out_sb).abs().mean()
|
||||
# print('OUT', err_fused, err_sb)
|
||||
|
||||
# err_fused = (standard[d].weight.grad - fused_mlp.linear2.weight.grad).abs().mean()
|
||||
# err_sb = (standard[d].weight.grad - sb[d].weight.grad).abs().mean()
|
||||
|
||||
# print('GW2', err_fused, err_sb)
|
||||
|
||||
# err_fused = (standard[0].weight.grad - fused_mlp.linear1.weight.grad).abs().mean()
|
||||
# err_sb = (standard[0].weight.grad - sb[0].weight.grad).abs().mean()
|
||||
|
||||
# print('GW1', err_fused, err_sb)
|
||||
|
||||
# err_fused = (x1.grad - x2.grad).abs().mean()
|
||||
# err_sb = (x1.grad - x3.grad).abs().mean()
|
||||
|
||||
# print('GX1', err_fused, err_sb)
|
||||
|
||||
# import pdb; pdb.set_trace()
|
||||
|
||||
|
||||
# # NO GELU, ST GRADIENTS, EVERYTHING FINE.
|
||||
|
|
@ -1,165 +0,0 @@
|
|||
|
||||
import torch
|
||||
import json
|
||||
from bitsandbytes.nn.triton_based_modules import SwitchBackGlobalMLP, SwitchBackGlobalLinear, StandardLinear
|
||||
import time
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
print('Startin')
|
||||
|
||||
|
||||
for dim in [1024, 1280, 1408, 1664, 2048]:
|
||||
for batch in [2**14, 2**15, 2**16, 2**17]:
|
||||
|
||||
x1 = torch.randn(batch, dim).cuda().requires_grad_(True)
|
||||
d = 2
|
||||
|
||||
standard = torch.nn.Sequential(
|
||||
torch.nn.LayerNorm(dim),
|
||||
torch.nn.Linear(dim, 4 * dim),
|
||||
torch.nn.GELU(),
|
||||
torch.nn.Linear(4 * dim, dim),
|
||||
).cuda()
|
||||
|
||||
my_standard = torch.nn.Sequential(
|
||||
torch.nn.LayerNorm(dim),
|
||||
StandardLinear(dim, 4 * dim),
|
||||
torch.nn.GELU(),
|
||||
StandardLinear(4 * dim, dim),
|
||||
).cuda()
|
||||
|
||||
fused_mlp = SwitchBackGlobalMLP(dim, 4 * dim).cuda()
|
||||
|
||||
sb = torch.nn.Sequential(
|
||||
torch.nn.LayerNorm(dim),
|
||||
SwitchBackGlobalLinear(dim, 4 * dim),
|
||||
torch.nn.GELU(),
|
||||
SwitchBackGlobalLinear(4 * dim, dim),
|
||||
).cuda()
|
||||
|
||||
standard_compiled = torch.compile(standard)
|
||||
|
||||
print('Model part 2')
|
||||
|
||||
repeat = 32
|
||||
|
||||
|
||||
info = {'repeat' : repeat, 'batch_size' : batch, 'dim' : dim}
|
||||
|
||||
k = 'standard'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_standard = standard(x1)
|
||||
((2 ** 16) * out_standard).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_standard = standard(x1)
|
||||
((2 ** 16) * out_standard).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
x1.grad.zero_()
|
||||
|
||||
k = 'my_standard'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_my_standard = my_standard(x1)
|
||||
((2 ** 16) * out_my_standard).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_my_standard = my_standard(x1)
|
||||
((2 ** 16) * out_my_standard).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
x1.grad.zero_()
|
||||
|
||||
k = 'standard_compiled'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_standard_compiled = standard_compiled(x1)
|
||||
((2 ** 16) * out_standard_compiled).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_standard_compiled = standard_compiled(x1)
|
||||
((2 ** 16) * out_standard_compiled).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
x1.grad.zero_()
|
||||
|
||||
k = 'sb'
|
||||
for _ in range(repeat // 2):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_sb = sb(x1)
|
||||
((2 ** 16) * out_sb).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
with torch.cuda.amp.autocast():
|
||||
out_sb = sb(x1)
|
||||
((2 ** 16) * out_sb).abs().mean().backward()
|
||||
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
ms = (end - start) / repeat * 1000
|
||||
print(f"time {k}: {ms:.3f} ms")
|
||||
info[k] = ms
|
||||
|
||||
|
||||
info_json = json.dumps(info)
|
||||
|
||||
|
||||
with open("tests/triton_tests/info_mlp_autocast_ln.jsonl", "a") as file:
|
||||
file.write(info_json + "\n")
|
||||
|
||||
|
||||
#exit()
|
||||
|
||||
# err_fused = (out_standard - out_fused).abs().mean()
|
||||
# err_sb = (out_standard - out_sb).abs().mean()
|
||||
# print('OUT', err_fused, err_sb)
|
||||
|
||||
# err_fused = (standard[d].weight.grad - fused_mlp.linear2.weight.grad).abs().mean()
|
||||
# err_sb = (standard[d].weight.grad - sb[d].weight.grad).abs().mean()
|
||||
|
||||
# print('GW2', err_fused, err_sb)
|
||||
|
||||
# err_fused = (standard[0].weight.grad - fused_mlp.linear1.weight.grad).abs().mean()
|
||||
# err_sb = (standard[0].weight.grad - sb[0].weight.grad).abs().mean()
|
||||
|
||||
# print('GW1', err_fused, err_sb)
|
||||
|
||||
# err_fused = (x1.grad - x2.grad).abs().mean()
|
||||
# err_sb = (x1.grad - x3.grad).abs().mean()
|
||||
|
||||
# print('GX1', err_fused, err_sb)
|
||||
|
||||
# import pdb; pdb.set_trace()
|
||||
|
||||
|
||||
# # NO GELU, ST GRADIENTS, EVERYTHING FINE.
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 119 KiB |
Binary file not shown.
Binary file not shown.
|
Before Width: | Height: | Size: 51 KiB |
|
|
@ -1,69 +0,0 @@
|
|||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
|
||||
import matplotlib.gridspec as gridspec
|
||||
|
||||
cmap=plt.get_cmap('cool')
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
fig = plt.figure(tight_layout=True, figsize=(6,3.5))
|
||||
gs = gridspec.GridSpec(1, 1)
|
||||
|
||||
|
||||
rdf = pd.read_json('tests/triton_tests/info.jsonl', lines=True)
|
||||
|
||||
ax = fig.add_subplot(gs[0, 0])
|
||||
|
||||
# now plot the % speedup for different batch sizes
|
||||
for j, batch_size in enumerate([2**14, 2**15, 2**16, 2**17]):
|
||||
all_xs, all_ys = [], []
|
||||
for k, marker, ls, color, name in [
|
||||
('x_quantize_rowwise+g_quantize_rowwise+w_quantize_global+w_quantize_global_transpose+standard_gw+global_fwd+global_bwd', 'o', '-', 'C4', 'SwitchBack int8 (total time)'),
|
||||
('x_quantize_rowwise+g_quantize_rowwise+w_quantize_global+w_quantize_global_transpose', 'o', '-', 'C4', 'SwitchBack int8 (total time)'),
|
||||
]:
|
||||
|
||||
xs, ys = [], []
|
||||
df = rdf[rdf.batch_size == batch_size]
|
||||
for embed_dim in [1024, 1280, 1408, 1664, 2048, 4096]:
|
||||
df_ = df[df.dim_in == embed_dim]
|
||||
df_ = df_[df_.dim_out == embed_dim * 4]
|
||||
xs.append(embed_dim)
|
||||
y_ = 0
|
||||
for k_ in k.split('+'):
|
||||
y_ += df_[k_].values[0]
|
||||
df_ = df[df.dim_in == embed_dim * 4]
|
||||
df_ = df_[df_.dim_out == embed_dim]
|
||||
for k_ in k.split('+'):
|
||||
y_ += df_[k_].values[0]
|
||||
ys.append(y_ * 0.5)
|
||||
all_xs.append(xs)
|
||||
all_ys.append(ys)
|
||||
|
||||
color = cmap(j * 0.25)
|
||||
real_ys = [100 * all_ys[1][i] / all_ys[0][i] for i in range(len(all_ys[0]))]
|
||||
markers = ['^', 'v', 'P', 'o']
|
||||
ax.plot(all_xs[0], real_ys, color=color, label=f'batch * sequence length = {batch_size}', marker=markers[j], markersize=5 if marker=='s' else 5)
|
||||
|
||||
ax.legend()
|
||||
ax.set_xlabel('dim', fontsize=13)
|
||||
ax.set_xscale('log')
|
||||
ax.grid()
|
||||
ax.set_ylabel(r'% time occupied by quantize ops', fontsize=12)
|
||||
|
||||
|
||||
ax.tick_params(axis='x', labelsize=11)
|
||||
ax.tick_params(axis='y', labelsize=11)
|
||||
|
||||
ax.set_xticks([1024, 2048, 4096])
|
||||
ax.set_xticklabels([1024, 2048, 4096])
|
||||
ax.set_xticks([], minor=True)
|
||||
|
||||
#ax.set_title(' Linear layer summary, varying dimensions', fontsize=10, loc='left', y=1.05, pad=-20)
|
||||
|
||||
|
||||
|
||||
plt.savefig('tests/triton_tests/plot2.pdf', bbox_inches='tight')
|
||||
|
||||
Binary file not shown.
Binary file not shown.
|
Before Width: | Height: | Size: 57 KiB |
|
|
@ -1,193 +0,0 @@
|
|||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import os
|
||||
import matplotlib.lines as mlines
|
||||
import matplotlib.gridspec as gridspec
|
||||
|
||||
cmap=plt.get_cmap('cool')
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
fig = plt.figure(tight_layout=True, figsize=(12,3.5))
|
||||
gs = gridspec.GridSpec(1, 3)
|
||||
|
||||
|
||||
rdf1 = pd.read_json('tests/triton_tests/info_mlp_autocast_ln.jsonl', lines=True)
|
||||
|
||||
ax = fig.add_subplot(gs[0, 0])
|
||||
|
||||
# now plot the % speedup for different batch sizes
|
||||
for j, batch_size in enumerate([2**15, 2**17]):#, 2**15, 2**17, 2**17]):
|
||||
all_xs, all_ys = {}, {}
|
||||
for k, marker, ls, color, name in [
|
||||
('standard_compiled', 'o', '-', 'C0', 'standard compiled (total time)'),
|
||||
#('standard', 'o', '-', 'C1', 'standard (total time)'),
|
||||
('my_standard', 'o', '-', 'C2', 'my standard (total time)'),
|
||||
('sb', 'o', '-', 'C4', 'SwitchBack int8 (total time)'),
|
||||
]:
|
||||
|
||||
xs, ys = [], []
|
||||
df = rdf1[rdf1.batch_size == batch_size]
|
||||
for embed_dim in [1024, 1280, 1408, 1664, 2048]:
|
||||
df_ = df[df.dim == embed_dim]
|
||||
xs.append(embed_dim)
|
||||
y_ = 0
|
||||
for k_ in k.split('+'):
|
||||
y_ += df_[k_].values[0]
|
||||
ys.append(y_)
|
||||
|
||||
all_xs[k] = xs
|
||||
all_ys[k] = ys
|
||||
#ax.plot(xs, ys, color=color, label=f'batch * sequence length = {batch_size}', marker=marker, markersize=5 if marker=='s' else 5)
|
||||
|
||||
|
||||
color= cmap(float(j))
|
||||
speedup_over_my_standard = [-100 * (all_ys['sb'][i] - all_ys['my_standard'][i]) / all_ys['my_standard'][i] for i in range(len(all_ys['my_standard']))]
|
||||
speedup_over_compile = [-100 * (all_ys['sb'][i] - all_ys['standard_compiled'][i]) / all_ys['standard_compiled'][i] for i in range(len(all_ys['standard_compiled']))]
|
||||
|
||||
ax.plot(xs, speedup_over_my_standard, color=color, label=f'batch * sequence length = {batch_size}', marker='o', markersize=5 if marker=='s' else 5)
|
||||
ax.plot(xs, speedup_over_compile, color=color, label=f'batch * sequence length = {batch_size}', marker='o', markersize=5 if marker=='s' else 5, linestyle='--')
|
||||
|
||||
|
||||
#ax.legend()
|
||||
ax.set_xlabel('dim', fontsize=13)
|
||||
ax.set_xscale('log')
|
||||
ax.grid()
|
||||
ax.set_ylabel(r'% speedup', fontsize=12)
|
||||
|
||||
ax.tick_params(axis='x', labelsize=11)
|
||||
ax.tick_params(axis='y', labelsize=11)
|
||||
|
||||
ax.set_xticks([1024, 2048])
|
||||
ax.set_xticklabels([1024, 2048])
|
||||
ax.set_xticks([], minor=True)
|
||||
ax.set_title('MLP Block', fontsize=10, loc='left', y=1.07, pad=-20)
|
||||
|
||||
|
||||
##########################################
|
||||
|
||||
rdf2 = pd.read_json('tests/triton_tests/attn_info_ln.jsonl', lines=True)
|
||||
|
||||
ax = fig.add_subplot(gs[0, 1])
|
||||
|
||||
for j, batch_size in enumerate([2**15, 2**17]):#, 2**15, 2**17, 2**17]):
|
||||
all_xs, all_ys = {}, {}
|
||||
for k, marker, ls, color, name in [
|
||||
('standard_compiled', 'o', '-', 'C0', 'standard compiled (total time)'),
|
||||
#('standard', 'o', '-', 'C1', 'standard (total time)'),
|
||||
('my_standard', 'o', '-', 'C2', 'my standard (total time)'),
|
||||
('sb', 'o', '-', 'C4', 'SwitchBack int8 (total time)'),
|
||||
]:
|
||||
|
||||
xs, ys = [], []
|
||||
df = rdf2[rdf2.batch_size == batch_size]
|
||||
for embed_dim in [1024, 1280, 1408, 1664, 2048]:
|
||||
df_ = df[df.dim == embed_dim]
|
||||
xs.append(embed_dim)
|
||||
y_ = 0
|
||||
for k_ in k.split('+'):
|
||||
y_ += df_[k_].values[0]
|
||||
ys.append(y_)
|
||||
|
||||
all_xs[k] = xs
|
||||
all_ys[k] = ys
|
||||
#ax.plot(xs, ys, color=color, label=f'batch * sequence length = {batch_size}', marker=marker, markersize=5 if marker=='s' else 5)
|
||||
|
||||
color= cmap(float(j))
|
||||
speedup_over_my_standard = [-100 * (all_ys['sb'][i] - all_ys['my_standard'][i]) / all_ys['my_standard'][i] for i in range(len(all_ys['my_standard']))]
|
||||
speedup_over_compile = [-100 * (all_ys['sb'][i] - all_ys['standard_compiled'][i]) / all_ys['standard_compiled'][i] for i in range(len(all_ys['standard_compiled']))]
|
||||
|
||||
ax.plot(xs, speedup_over_my_standard, color=color, label=f'batch * sequence length = {batch_size}', marker='o', markersize=5 if marker=='s' else 5)
|
||||
ax.plot(xs, speedup_over_compile, color=color, label=f'batch * sequence length = {batch_size}', marker='o', markersize=5 if marker=='s' else 5, linestyle='--')
|
||||
|
||||
|
||||
speedup_compiled = mlines.Line2D([], [], linestyle='--', color='gray', label='speedup over compiled')
|
||||
speedup_baseline = mlines.Line2D([], [], linestyle='-', color='gray', label='speedup over baseline')
|
||||
batch_size_4 = mlines.Line2D([], [], linestyle='-', color=cmap(0.), label=f'batch = {int(2**15 // 256)}, sequence = {256}')
|
||||
batch_size_8 = mlines.Line2D([], [], linestyle='-', color=cmap(1.), label=f'batch = {int(2**17 / 256)} sequence = {256}')
|
||||
|
||||
# Create the legend with the proxy artists
|
||||
|
||||
# adjust plots so that they dont get squished by putting the legend under both
|
||||
|
||||
|
||||
plt.subplots_adjust(left=0.2)
|
||||
plt.subplots_adjust(right=0.8)
|
||||
|
||||
fig.legend(handles=[speedup_compiled, speedup_baseline, batch_size_4, batch_size_8], ncol=2, loc='upper center', bbox_to_anchor=(0.35, 0.255))
|
||||
|
||||
ax.set_xlabel('dim', fontsize=13)
|
||||
ax.set_xscale('log')
|
||||
ax.grid()
|
||||
ax.set_ylabel(r'% speedup', fontsize=12)
|
||||
|
||||
ax.tick_params(axis='x', labelsize=11)
|
||||
ax.tick_params(axis='y', labelsize=11)
|
||||
|
||||
ax.set_xticks([1024, 2048])
|
||||
ax.set_xticklabels([1024, 2048])
|
||||
ax.set_xticks([], minor=True)
|
||||
|
||||
ax.set_title('Attention Block', fontsize=10, loc='left', y=1.07, pad=-20)
|
||||
|
||||
|
||||
|
||||
##########################################
|
||||
|
||||
|
||||
|
||||
ax = fig.add_subplot(gs[0, 2])
|
||||
|
||||
for j, batch_size in enumerate([2**15]):#, 2**15, 2**17, 2**17]):
|
||||
all_xs, all_ys = {}, {}
|
||||
for k, marker, ls, color, name, b in [
|
||||
('standard_compiled', 'o', '-', 'C0', 'standard compiled (total time)', False),
|
||||
('standard_compiled', 'o', '-', 'C0', 'standard compiled (total time)', True),
|
||||
|
||||
#('standard', 'o', '-', 'C1', 'standard (total time)'),
|
||||
#('my_standard', 'o', '-', 'C2', 'my standard (total time)'),
|
||||
('attn', 'o', '-', 'C4', 'SwitchBack int8 (total time)', True),
|
||||
]:
|
||||
rdf = rdf2 if b else rdf1
|
||||
|
||||
xs, ys = [], []
|
||||
df = rdf[rdf.batch_size == batch_size]
|
||||
for embed_dim in [1024, 1280, 1408, 1664, 2048]:
|
||||
df_ = df[df.dim == embed_dim]
|
||||
xs.append(embed_dim)
|
||||
y_ = 0
|
||||
for k_ in k.split('+'):
|
||||
y_ += df_[k_].values[0]
|
||||
ys.append(y_)
|
||||
|
||||
all_xs[k + str(int(b))] = xs
|
||||
all_ys[k + str(int(b))] = ys
|
||||
#ax.plot(xs, ys, color=color, label=f'batch * sequence length = {batch_size}', marker=marker, markersize=5 if marker=='s' else 5)
|
||||
|
||||
|
||||
print(all_ys.keys())
|
||||
all_ys['standard_compiled'] = [x + y for x, y in zip(all_ys['standard_compiled0'], all_ys['standard_compiled1'])]
|
||||
|
||||
speedup_over_my_standard = [100 * all_ys['attn1'][i] / (all_ys['standard_compiled'][i] + all_ys['attn1'][i]) for i in range(len(all_ys['standard_compiled']))]
|
||||
ax.plot(xs, speedup_over_my_standard, color='gold', label=r'% time occupied by attention', marker='H', markersize=8)
|
||||
|
||||
speedup_over_my_standard = [100 * all_ys['standard_compiled1'][i] / (all_ys['standard_compiled0'][i] + all_ys['standard_compiled1'][i]) for i in range(len(all_ys['standard_compiled']))]
|
||||
ax.plot(xs, speedup_over_my_standard, color='indianred', label=r'% time occupied by attention block', marker='P', markersize=8)
|
||||
|
||||
|
||||
ax.legend(bbox_to_anchor=(1.02, -0.27))
|
||||
ax.set_xlabel('dim', fontsize=13)
|
||||
ax.set_xscale('log')
|
||||
ax.grid()
|
||||
ax.set_ylabel(r'% time', fontsize=12)
|
||||
|
||||
ax.tick_params(axis='x', labelsize=11)
|
||||
ax.tick_params(axis='y', labelsize=11)
|
||||
|
||||
ax.set_xticks([1024, 2048])
|
||||
ax.set_xticklabels([1024, 2048])
|
||||
ax.set_xticks([], minor=True)
|
||||
|
||||
plt.savefig('tests/triton_tests/plot3.pdf', bbox_inches='tight')
|
||||
|
||||
|
|
@ -1,43 +0,0 @@
|
|||
|
||||
import time
|
||||
import torch
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import bitsandbytes.nn as bnn
|
||||
from bitsandbytes.nn.triton_based_modules import SwitchBackLinear, SwitchBackGlobalLinear
|
||||
|
||||
from bitsandbytes.nn.triton_utils.v0.quantize_rowwise_nogroup import quantize_rowwise_nogroup
|
||||
|
||||
|
||||
# 256 * 256 * 4096 _> 0.7
|
||||
# 256 * 128 * 8192 -> 10
|
||||
if __name__ == '__main__':
|
||||
torch.manual_seed(0)
|
||||
|
||||
# hparams
|
||||
repeat = 16
|
||||
dim=8192
|
||||
layers = 4
|
||||
|
||||
batch_size = 256 * 128
|
||||
|
||||
# simulate forward pass
|
||||
x = torch.randn(batch_size, dim, dtype=torch.float16).cuda()
|
||||
|
||||
for _ in range(repeat // 2):
|
||||
quantize_rowwise_nogroup(x)
|
||||
|
||||
torch.cuda.synchronize()
|
||||
start = time.time()
|
||||
for _ in range(repeat):
|
||||
quantize_rowwise_nogroup(x)
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
|
||||
print(f"time: {(end - start) / repeat * 1000:.3f} ms")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user